CN113240588A

CN113240588A - Image defogging and exposure method based on enhanced atmospheric scattering model

Info

Publication number: CN113240588A
Application number: CN202110044013.9A
Authority: CN
Inventors: 鞠铭烨; 王欣; 刘菊萍; 张登银
Original assignee: Nanjing University of Posts and Telecommunications
Current assignee: Nanjing University of Posts and Telecommunications
Priority date: 2021-01-13
Filing date: 2021-01-13
Publication date: 2021-08-10
Anticipated expiration: 2041-01-13
Also published as: CN113240588B

Abstract

The invention discloses an image defogging and exposure method based on an enhanced atmospheric scattering model, belonging to the technical field of image processing, and the method provides an image defogging system based on the enhanced atmospheric scattering model, wherein the system comprises the following modules: an image input module: for inputting an original image; an image processing module: the system is used for defogging the original image; an image output module: the image processing device is used for outputting a defogged image corresponding to the original image; the method comprises the steps of firstly, inputting an original image through an image input module; then, defogging the original image through an image processing module; and finally, outputting the defogged image corresponding to the original image through an image output module: the method has better modeling capability and higher performance in the aspects of image defogging processing efficiency and recovery quality.

Description

Image defogging and exposure method based on enhanced atmospheric scattering model

Technical Field

The invention belongs to the technical field of image processing, and particularly relates to an image defogging and exposure method based on an enhanced atmospheric scattering model.

Background

Haze is a common natural phenomenon in the real world, and for information acquisition industries such as image information acquisition, the appearance of haze can seriously reduce the contrast between image information and real information and change the inherent color of an image. Not only haze, but also the contrast between image information and real information can be reduced in any weather which can affect the quality of the acquired image, and especially outdoor scene shooting is performed in worse severe weather, and a low-contrast image (the low-contrast image can be called as a foggy image) shot under the above conditions does not usually have enough information to ensure the normal operation of a visual system, so that how to recover the foggy image so as to ensure the real effectiveness of image information extraction is of great importance.

The most intuitive method for restoring the foggy image is the traditional enhancement method, which realizes restoration of the foggy image by increasing local or global contrast, and the traditional enhancement method comprises Retinex, a histogram equalization method and the like, but the traditional enhancement method ignores the physical degradation process of the foggy image, so the visual quality of the restoration result is limited. Further, in order to make up for the above-mentioned shortcomings, an image defogging method based on an Atmospheric Scattering Model (ASM) has been proposed, which can generate high-quality results by making full use of additional information, and the Atmospheric Scattering Model (ASM) is widely used to describe the degradation process of a single blurred image, and mathematically, the modeling thereof is as follows:

I(x，y)＝A·ρ(x，y)·t(x，y)+A·(1-t(x，y))

in the above formula, (x, y) represents the position coordinates of the foggy image I, a represents the atmospheric light value, ρ (x, y) represents the scene reflectivity in a fogless scene, t (x, y) represents the atmospheric transmittance, and when the particles suspended in the atmosphere are uniformly distributed in space, t (x, y) is e^{-β·d(x，y)}Where β represents the scattering coefficient (also called the haze concentration influence coefficient) and d (x, y) represents the scene depth.

The above-described image defogging methods based on atmospheric scattering models include dark channel methods, fog line methods, and the like, but unfortunately, these methods require expensive costs and other additional preconditions, thereby limiting the usefulness of the method in many applications.

The above-mentioned single image defogging method has made an important progress in the past decades, and as the information technology has been continuously innovated with the passage of the era, the current image defogging method is mainly classified into a priori-based technology and a learning-based technology. The core idea of the prior art-based method is to estimate imaging parameters by using potential prior knowledge to reduce uncertainty of scene depth, and then combine an Atmospheric Scattering Model (ASM) based method to recover a fog-free result. There is also an hl (haze lines) algorithm that uses fog lines to determine atmospheric light and atmospheric transmittance, thereby achieving image defogging. There are also a priori Gamma corrected based Image defogging method (IDGCP) algorithms that use Gamma corrected priors to synthesize a virtual transformation of a foggy Image, and then design global defogging by extracting scene depth from the transformation and foggy Image. The image processed by the method for the foggy image has clear result and small loss of details, but if the parameters are not accurate in the defogging process, the method can cause the result of the image processing to be not ideal.

The core idea of the method based on the learning technology is to utilize a neural network to extract the characteristics of the foggy image without artificially designing an image characteristic extractor, and a system based on a convolutional neural network is called a DehazeNet algorithm under the inspiration of famous image prior, and the method realizes end-to-end image defogging. There is also a Multi-scale Convolutional Neural network (MSCNN) algorithm that establishes a Convolutional Neural Network (CNN) to improve image recovery performance by learning more features. The fog image processed by the method has good processing result, but has higher requirements on the processing capacity and the memory of a computing platform and higher cost.

Therefore, it is important to propose an image defogging technology with higher robustness to eliminate the adverse effect of the low-contrast image and reconstruct the low-contrast image blur information.

Disclosure of Invention

The purpose of the invention is as follows: in order to overcome the defects that the defogging effect of an image is not ideal, the operation expense of a defogging algorithm is large and the like caused by dim images and the like after the defogging of the defogged image in the prior art, the invention provides the image defogging and exposure method based on the enhanced atmospheric scattering model.

The technical scheme is as follows: in order to achieve the above object, the image defogging and exposure method based on the enhanced atmospheric scattering model of the present invention provides an image defogging system based on the enhanced atmospheric scattering model, which comprises the following modules:

an image input module: for reading an original image I;

an image processing module: the system is used for carrying out defogging treatment on the original image I;

an image output module: the image processing device is used for outputting a defogged image I' corresponding to the original image I;

the method comprises the following steps:

s1, firstly, reading an original image I through an image input module;

s2, defogging the original image I through the image processing module;

s3 finally, the image output module outputs the defogged image I' corresponding to the original image I.

Further, the original image I is not limited to only a foggy image.

Further, the image processing module includes an EASM module that provides an EASM model whose expression is as follows:

i (x, y) ═ a · (1- α (x, y)). ρ (x, y) · t (x, y) + a · (1-t (x, y)) formula (1)

In the above formula (1), (x, y) indicates the position coordinates of the point in the original image I, a indicates the atmospheric light value, ρ (x, y) indicates the scene reflectance of the point having the position coordinates (x, y) in the fog-free scene, t (x, y) indicates the atmospheric transmittance of the point having the position coordinates (x, y), and α (x, y) indicates the light absorption coefficient of the point having the position coordinates (x, y).

Further, the air conditioner is provided with a fan,

order to

Wherein t is_minIs the minimum value of t (x, y);

then the above equation (1) etc. is changed to equation (2):

in the above formula (2), (x, y) denotes the position coordinates of the point in the original image I, a denotes the atmospheric light value of the original image I, ρ (x, y) denotes the scene reflectance of the point with the position coordinates (x, y) in the fog-free scene, t (x, y) denotes the atmospheric transmittance of the point with the position coordinates (x, y), α (x, y) denotes the light absorption coefficient of the point with the position coordinates (x, y), and t (x, y) denotes the light absorption coefficient of the point with the position coordinates (x, y)_minIs the minimum value of t (x, y).

Further, the air conditioner is provided with a fan,

converting equation (2) to equation (3):

in the above-mentioned formula (3),

the average value of the atmospheric light values of the original image I on the R, G, B three color channels is calculated by equation (4):

in the above formula (4), c is the color channel index, c ∈ { R, G, B } refers to selecting R, G, B three color channels, A^RFor the atmospheric light value of the original image I on the R color channel, A^GFor the atmospheric value of the original image I on the G color channel, A^BThe atmospheric light value of the original image I on the B color channel is obtained;

in the above-mentioned formula (3),(x, y) refers to the position coordinates of the points in the original image I,

it is the local mean value of the point with position coordinate (x, y) on R, G, B three color channels, and is calculated by formula (5):

in the above formula (5), Ω (x, y) is a set of points in the neighborhood centered on coordinates (x, y), | Ω (x, y) | is the total number of points in the set Ω (x, y), c is a color channel index, c ∈ { R, G, B } refers to R, G, B three color channels selected, I ∈ { R, G, B }, where I is a color channel index, and I is a color channel index^R(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values on the R color channel of a point with position coordinates (x ', y'), I^G(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values on the G color channel of a point with position coordinates (x ', y'), I^B(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values of a point with a position coordinate of (x ', y') on the B color channel, and a component value of the point with the position coordinate of (x ', y') on the R color channel, a component value of the point with the position coordinate of (x ', y') on the G color channel, and a component value of the point with the position coordinate of (x ', y') on the B color channel are directly obtained through software;

in the above formula (3), (x, y) refers to the position coordinates of the point in the original image I,

the average of the scene reflectivities at R, G, B for a point with location coordinates (x, y) in a fog-free scene is calculated by equation (6):

in the above equation (6), Ω (x, y) is a set of points in the neighborhood centered on the coordinates (x, y)In the formula, | Ω (x, y) | is the total number of the midpoint in the set Ω (x, y), C is the color channel index, C ∈ { R, G, B } refers to R, G, B three color channels, ρ^R(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values, ρ, of a point with position coordinate (x ', y') on the R color channel in a fog-free scene^G(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values on the G color channel of a point with position coordinate (x ', y') in the fog-free scene, ρ^B(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values of a point with a position coordinate (x ', y') on the B color channel in the fog-free scene, and the component value of the point with the position coordinate (x ', y') on the R color channel in the fog-free scene, the component value of the point with the position coordinate (x ', y') on the G color channel in the fog-free scene, and the component value of the point with the position coordinate (x ', y') on the B color channel in the fog-free scene are directly obtained through software.

Further, the air conditioner is provided with a fan,

order to

Fitting a logarithmic function with a rational function according to equation (7):

in the above formula (7), p₁And p₂Are all parameters of rational functions;

according to

And formula (7) converting the formula (3) into formula (8):

converting equation (8) and the like to equation (9):

λ₁·(t(x，y))²+λ₂(x，y)·t(x，y)+λ₃(x, y) ═ 0 equation (9)

In the above-mentioned formula (9),

solving t (x, y) according to equation (9), t (x, y) is then expressed as equation (10):

then, using a guided filter, equation (10) is converted to equation (11):

in the above equation (11), F (·) represents a pilot filter operator;

equation (11) is simplified to equation (12):

in the above-mentioned formula (12),

is the average of the atmospheric light values of the original image I over R, G, B three color channels,

calculating by formula (4);

is the local mean of a point with position coordinates (x, y) on R, G, B three color channels,

calculated by formula (5); p is a radical of₁And p₂Are all modeled by formula (7)Obtaining; phi (-) is a calculation formula for the atmospheric transmittance of a point with position coordinates (x, y);

combining the formula (12) and the formula (2), obtaining a scene reflectivity recovery formula of a point with the position coordinates (x, y) under the fog-free scene, and referring to the formula (13):

in the above formula (13), SF (-) is a scene reflectivity recovery formula, and a is an atmospheric light value of the original image I;

refers to the average value of the atmospheric light values of the original image I at R, G, B three color channels,

calculating by formula (4);

calculated by formula (5); p is a radical of₁And p₂Are obtained by simulation of equation (7).

Further, the air conditioner is provided with a fan,

the image processing module also comprises an IDE module which provides a global stretching strategy calculation t of a golden section method in a one-dimensional search algorithm_minReferring to equation (14):

in the above formula (14), argmin {. is a minimum function; Ψ (-) is a saturation operator function; epsilon is a preset defogging result saturation; SF (-) is the scene reflectivity recovery formula; a being the original image IAn atmospheric light value; ↓^δIs the down-sampling operator with coefficient delta, (I) ↓δand

The method is characterized in that the corresponding image or matrix in brackets is down-sampled to 1/delta of the original resolution;

calculating by formula (4); p is a radical of₁And p₂Are obtained by simulation of equation (7).

Further, t obtained from the formula (14)_minThe mathematical model corresponding to the defogged image I 'is obtained by substituting the formula (13) ρ (x, y), and the defogged image I' is ρ (x, y).

Further, δ is 4, and ∈ is 0.02.

Further, the air conditioner is provided with a fan,

the S2 includes the steps of:

s21, firstly, the image processing module receives an original image I;

s22, the EASM module performs mathematical transformation on the original image I to obtain a corresponding mathematical transformation formula, and then the EASM module performs mathematical processing on the mathematical transformation formula;

s23 then determining the t by the IDE module providing a global stretching strategy of the golden section method in the one-dimensional search algorithm_min；

And S24, outputting the defogged image I' corresponding to the mathematical model after mathematical processing to an image output module for the next output work.

Has the advantages that: compared with the prior art, the invention has the advantages that:

1. after the image is defogged, the defogging effect of the image is not obvious, and the image is still dim after the defogging, the method can effectively improve the defects, and the image after the defogging is closer to real information, is more accurate and has better defogging effect;

2. the prior art has high running time cost for image defogging, so that the running expense is high, the time cost is greatly reduced, and the image defogging efficiency is improved;

3. according to the method, the light absorption coefficient alpha is introduced into a traditional atmospheric scattering model ASM to obtain an enhanced atmospheric scattering model EASM, the enhanced atmospheric scattering model EASM can overcome the defects of poor defogged image effect and the like caused by dim images and the like after defogging caused by the traditional atmospheric scattering model ASM, a foggy scene can be better simulated, the images after defogging are enabled to better accord with real scene information, the EASM has better modeling capacity for the blurred images, and the method has higher performance in the aspects of processing efficiency and recovery quality;

4. the method provides the IDE module, and the IDE module can simultaneously carry out defogging and exposure on a single foggy image without post-processing based on a global stretching strategy, thereby effectively reducing the running time cost and improving the processing efficiency of the foggy image; the IDE module does not need any training process or extra information related to scene depth, and tools or calculation formulas used by the IDE module are simple to operate, so that the high efficiency and strong robustness of the method are ensured; and the IDE module improves the visibility of the hazy image.

Drawings

FIG. 1 is a diagram of a system architecture provided by the method of the present invention.

FIG. 2 is a flow chart of the method steps of the present invention.

Fig. 3 is an original image I in an embodiment of the method of the invention.

Fig. 4 is a dark channel image corresponding to the original image I of fig. 3.

Fig. 5 is a diagram of the defogging effect obtained after the defogging treatment of fig. 3 by using the HL method.

Fig. 6 is a diagram of the defogging effect obtained after the defogging treatment of fig. 3 by using the IDGCP method.

Fig. 7 is a graph showing the defogging effect obtained after the defogging treatment of fig. 3 by the DehazeNet method.

Fig. 8 is a graph of the defogging effect obtained after the defogging process of fig. 3 is performed by the MSCNN method.

FIG. 9 is a graph showing the defogging effect obtained after the defogging treatment of FIG. 3 by the method of the present invention.

FIG. 10 is a quantitative comparative visual chart of defogging effects of the HL method, the IDGCP method, the DehazeNet method, the MSCNN method and the 5 methods of the invention after processing the image in FIG. 3.

FIG. 11 is a schematic diagram of component values of points in an image on an R color channel, a G color channel, and a B color channel, respectively, obtained by MATLAB software.

Detailed Description

The present invention will be further described with reference to the accompanying drawings.

The first embodiment is as follows:

an enhanced atmospheric scattering model-based image defogging and exposure method of the embodiment provides an enhanced atmospheric scattering model-based image defogging system, and referring to fig. 1, the system comprises the following modules:

an image input module: the method is used for reading an original image I, wherein the original image I refers to image information obtained by outdoor scene information acquisition under the condition of haze weather or worse severe weather, and is not limited to a foggy image;

an image processing module: the image processing device is used for carrying out defogging treatment on the original image I to obtain a defogged image I';

an image output module: the defogged image I' corresponding to the original image I is output;

therefore, based on the above enhanced atmospheric scattering model image defogging system, the method of the embodiment includes the following steps: firstly, reading an original image I through an image input module; then, the image input module sends the input original image I to the image processing module, and the image processing module carries out defogging processing on the original image I; and finally, the image processing module sends the defogged image I 'to the image output module, and the image output module outputs the defogged image I' corresponding to the original image I.

Example two:

in this embodiment, based on the first embodiment, the image processing module includes an EASM module, and the EASM module is configured to perform mathematical processing transformation on an original image I to obtain a defogged image I' corresponding to the original image I.

The EASM module provides an EASM Model, and the EASM Model is an Enhanced Atmospheric Scattering Model EASM (EASM) obtained by improving a traditional Atmospheric Scattering Model ASM, wherein the traditional Atmospheric Scattering Model ASM is as follows:

I(x，y)＝A·ρ(x，y)·t(x，y)+A·(1-t(x，y))

in the above formula, (x, y) represents a spatial position coordinate of the original image I, a represents an atmospheric light value, ρ (x, y) represents a scene reflectance of a point having a position coordinate of (x, y) in a fog-free scene, t (x, y) represents an atmospheric transmittance of a point having a position coordinate of (x, y), and when particles suspended in the atmosphere are uniformly distributed in space, t (x, y) ═ e^{-β·d(x，y)}Where β represents a scattering coefficient (also referred to as a fog concentration influence coefficient), and d (x, y) represents a scene depth of a point whose position coordinate is (x, y).

The model expression of the enhanced atmospheric scattering model EASM described above refers to equation (1):

The model expression formula (1) of the enhanced atmospheric scattering model EASM is obtained by adding a light absorption coefficient α (x, y) to an atmospheric scattering model ASM, where in the formula (1), (x, y) denotes a spatial position coordinate of an original image I, α (x, y) denotes a light absorption coefficient of a point whose position coordinate is (x, y), a denotes an atmospheric light value, ρ (x, y) denotes a scene reflectivity of a point whose position coordinate is (x, y) in a fog-free scene, t (x, y) denotes an atmospheric transmittance of a point whose position coordinate is (x, y), and when particles suspended in the atmosphere are uniformly distributed in the space, t (x, y) ═ e^{-β·d(x，y)}Where β represents a scattering coefficient (also referred to as a fog concentration influence coefficient), and d (x, y) represents a scene depth of a point whose position coordinate is (x, y).

When the image processing module receives the original image I, firstly, the original image I is subjected to mathematical replacement through the enhanced atmospheric scattering model EASM to obtain a mathematical conversion formula corresponding to the original image I, and then, the mathematical conversion formula is further processed to obtain a mathematical model corresponding to the defogged image of the original image I, so that the defogged image of the original image I is obtained.

Example three:

in the embodiment, an image defogging and exposure method based on an enhanced atmospheric scattering model is provided, which comprises the following steps of based on the second embodiment,

the model expression of the enhanced atmospheric scattering model EASM refers to equation (1):

In the above formula (1), (x, y) refers to the spatial position coordinates of the original image I, a refers to the atmospheric light value, α (x, y) refers to the light absorption coefficient of the point whose position coordinate is (x, y), ρ (x, y) refers to the scene reflectance of the point whose position coordinate is (x, y) in the fog-free scene, and t (x, y) refers to the atmospheric transmittance of the point whose position coordinate is (x, y);

let the above-mentioned

Wherein t is_minIs the minimum value of t (x, y) which represents the atmospheric transmittance of a point with the position coordinate (x, y), and when the particles suspended in the atmosphere are uniformly distributed in the space, t (x, y) is equal to e^{-β·d(x，y)}Where β represents the scattering coefficient, d (x, y) represents the scene depth of a point with position coordinates (x, y), t_min＝e^-β·max(d)；

According to the above

Converting equation (1) to equation (2):

in the above formula (2), (x, y) refers to the spatial position coordinates of the original image I, a refers to the atmospheric light value, ρ (x, y) refers to the scene reflectivity of the point with the position coordinates (x, y) in the fog-free scene, t (x, y) refers to the atmospheric transmittance of the point with the position coordinates (x, y), and t is_minIs the minimum value of t (x, y);

in the above formula (2), the atmospheric light value a of the original image I is obtained by the following method: calculating the original image I by a He method to obtain a dark channel image corresponding to the original image I, and then processing the dark channel image to obtain an atmospheric light value;

the He method is adopted because the known He method finds that the brightness value of at least one color channel is very low (close to 0) for some pixel points in a local area of a non-sky part in a natural image after statistics on a large number of fog-free images is carried out; the expression of the known hazy image ASM model is: i (x, y) ═ a · ρ (x, y) · t (x, y) + a · (1-t (x, y)), the product of the atmospheric light value a and the scene reflectance ρ (x, y) in a fog-free scene is denoted by J (x, y), and J (x, y) ═ a · ρ (x, y) represents a fog-free image; accordingly, a dark channel prior model assumption can be obtained, that is, for any pair of natural fog-free images, the dark channel satisfies:

where Ω (x, y) is a set of pixel points centered at coordinates (x, y), c is the color channel index, J^R、J^G、J^BRefers to three color channels of a fog-free image expressed using RGB; assuming a dark channel prior model, the dark channel pixel values of the fog-free image are usually small, substantially approaching 0, but the dark channel pixel values of the fog-containing image are substantially close to the fog density values, making it easier to calculate the atmospheric light values.

As described above, the atmospheric light value a of the original image I is obtained by: firstly, obtaining a dark channel image of an original image I according to a He method; then, the dark channel image is divided into pixel points, and each pixel point has a corresponding imageThe prime value; then arranging all pixel points in the dark channel image according to the sequence of pixel values from large to small; finally, selecting pixel points with pixel values in a certain proportion in front, setting n selected pixel points, corresponding the spatial position coordinates of the pixel points in the dark channel image to the original image I to obtain the pixel points of the corresponding spatial position coordinates in the original image I, and setting the pixel values of the pixel points in R, G, B three color channels as X^R、X^c、X^BThen the atmospheric light value of the original image I in the R color channel

Atmospheric light value of original image I in G color channel

Atmospheric light value of original image I in B color channel

Thus obtaining the atmospheric light value of the original image I

When the image processing module receives the original image I, firstly, the original image I is subjected to mathematical replacement through the formula (2) to obtain a mathematical conversion formula corresponding to the original image I, and then, the mathematical conversion formula is further processed to obtain a mathematical model corresponding to the image of the original image I after defogging, so that the defogged image of the original image I is obtained.

Example four

In the embodiment, based on the third embodiment,

converting equation (2) to equation (3):

in the above-mentioned formula (3),

the average value of the atmospheric light values of the original image I on R, G, B three color channels, that is, the average value of the atmospheric light value of the original image I on the R color channel, the atmospheric light value of the original image I on the G color channel, and the atmospheric light value of the original image I on the B color channel;

calculated by equation (4):

according to the method, firstly, a dark channel image of an original image I is obtained according to a He method; then, dividing the dark channel image into pixel points, wherein each pixel point has a corresponding pixel value; then arranging all pixel points in the dark channel image according to the sequence of pixel values from large to small, selecting pixel points with pixel values in a certain proportion in front, setting n selected pixel points, and corresponding the spatial positions of the pixel points in the dark channel image to the original image I; finally, obtaining pixel points of the space position corresponding to the original image I, and setting the pixel values of the pixel points on R, G, B three color channels as X respectively^R、X^G、X^BThen the atmospheric light value of the original image I in the R color channel

Original image I in G colorAtmospheric light value on the tunnel

Atmospheric light value of original image I in B color channel

Thus obtaining

Namely, it is

Inputting an original image I into an image processing module, correspondingly outputting atmospheric light values of the original image I on three color channels by the image processing module, and then carrying out average algorithm processing on the atmospheric light values of the three color channels to obtain an atmospheric light value

refers to the local mean of a point with position coordinates (x, y) on R, G, B three color channels,

calculated by the formula (5),

in the above equation (5), Ω (x, y) is a set of points in the neighborhood centered on the coordinates (x, y)In the formula, | Ω (x, y) | is the total number of the midpoint in the set Ω (x, y), c is the color channel index, c ∈ { R, G, B } refers to R, G, B three color channels, I^R(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values on the R color channel of a point with position coordinates (x ', y'), I^G(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values on the G color channel of a point with position coordinates (x ', y'), I^B(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values of a point with a position coordinate of (x ', y') on the B color channel, and the component value of the point with a position coordinate of (x ', y') on the R color channel, the component value of the point with a position coordinate of (x ', y') on the G color channel, and the component value of the point with a position coordinate of (x ', y') on the B color channel are obtained by direct software acquisition, specifically by MATLAB software acquisition,

referring to fig. 11, fig. 11 is component values of a point on an R color channel, a G color channel, and a B color channel of an image respectively obtained by MATLAB software, fig. 11(a) is a certain original image, fig. 11(B) is a component value of a point on an R color channel of the original image, fig. 11(c) is a component value of a point on a G color channel of the original image, and fig. 11(d) is a component value of a point on a B color channel of the original image.

the average value of the scene reflectivity on R, G, B color channels, which represents a point with position coordinates (x, y) in a fog-free scene, is calculated by equation (6):

in the above equation (6), Ω (x, y) is a set of points in the neighborhood centered on the coordinates (x, y), | Ω (x, y) | is the total number of points in the set Ω (x, y), c is a color channel index, and c ∈ { R, G, B } means R, G, B three choicesIndividual color channel, p^R(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values, ρ, of a point with position coordinate (x ', y') on the R color channel in a fog-free scene^G(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values on the G color channel of a point with position coordinate (x ', y') in the fog-free scene, ρ^B(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values of a point with a position coordinate (x ', y') on the B color channel in the fog-free scene, and the component value of the point with the position coordinate (x ', y') on the R color channel in the fog-free scene, the component value of the point with the position coordinate (x ', y') on the G color channel in the fog-free scene, and the component value of the point with the position coordinate (x ', y') on the B color channel in the fog-free scene are directly obtained through software, specifically, obtained through MATLAB software.

According to MATLAB simulation calculation to obtain

In this embodiment, let

in the above formula (7), p₁And p₂All are parameters of rational functions and can be obtained through MATLAB simulation fitting;

jiang

And formula (7) is substituted into formula (3), then the conversion is to formula (8):

the variables of equation (8) have the same meaning as the variables of equation (3);

continuing to convert equation (8) to equation (9):

λ₁·(t(x，y))²+λ₂(x，y)·t(x，y)+λ₃(x, y) ═ 0 equation (9)

In the above-mentioned formula (9),

then, t (x, y) is solved according to equation (9), and t (x, y) is expressed by equation (10):

to avoid assuming atmospheric reflectivity as a constant, resulting in image depth discontinuities at times, guided filtering is employed

And a directional filter for converting equation (10) to equation (11):

in the above equation (11), F (·) represents a pilot filter operator;

equation (11) is simplified to equation (12):

in the above equation (12), φ (-) is a calculation equation of the atmospheric transmittance of the point having the position coordinates (x, y), ln (t)_min)，

p₁，p₂Of the five variables, the number of variables,

r, G, B three color channels for original image IIs measured in the ambient light measurement of (a),

calculating by formula (4);

calculated by formula (5); p is a radical of₁And p₂Are obtained by simulation of a formula (7); phi (-) is a calculation formula for the atmospheric transmittance of a point with position coordinates (x, y);

then, combining the formula (12) and the formula (2), obtaining a scene reflectivity recovery formula of a point with the position coordinate (x, y) under the fog-free scene, wherein the recovery formula of the scene reflectivity ρ (x, y) is used for scene defogging and exposure, and the recovery formula of the scene reflectivity ρ (x, y) refers to the formula (13):

in the above equation (13), SF (-) is Scene reflectivity Recovery equation (SARF) for defogging and exposure, A, I, ln (t)_min)，

p₁，p₂Of the 7 variables:

wherein A refers to the atmospheric light value of the original image I, and the atmospheric light value A of the original image I is obtained by the following method: firstly, obtaining a dark channel image of an original image I according to a He method; then, dividing the dark channel image into pixel points, wherein each pixel point has a corresponding pixel value; then arranging all pixel points in the dark channel image according to the sequence of pixel values from large to small; finally, selecting a certain proportion of pixel points with the pixel values in front, setting n selected pixel points, and setting the spatial positions of the pixel points in the dark channel imageThe mark corresponds to the original image I to obtain pixel points of the corresponding space position coordinates in the original image I, and the pixel values of the pixel points in R, G, B three color channels are respectively set as X^R、X^G、X^BThen the atmospheric light value of the original image I in the R color channel

Atmospheric light value of original image I in G color channel

Atmospheric light value of original image I in B color channel

Thus obtaining the atmospheric light value of the original image I

Can be obtained by calculation of formula (4);

original image I: inputting an original image I, wherein the size of the original image I is I multiplied by j, performing mathematical transformation on the original image I in an image processing module to obtain a matrix of the original image I,

wherein [ R ] is_i，j，G_i，j，B_i，j]The component values of the point with the position coordinate (I, j) in the original image I on R, G, B three color channels respectively are referred, and the component values of the point in the original image I on R, G, B three color channels respectively can be directly obtained through software, in this embodiment, directly obtained through MATLAB software;

can be obtained by calculation of formula (5);

parameter p₁And p₂Can be obtained by fitting of equation (7).

The EASM module of this embodiment processes the original image I through the above steps, and when the image processing module receives the original image I, the image processing module performs mathematical replacement on the original image I to obtain a defogged image I 'of a mathematical model corresponding to an image in which the original image I is defogged, where the image I' is defogged — ρ (x, y), so as to obtain a defogged image of the original image I.

Example five:

based on the fourth embodiment, the image processing module further includes an IDE module, and the IDE module is configured to further process a processing result of the EASM model to obtain a mathematical model corresponding to the image of the original image I after defogging, so as to obtain a defogged image of the original image I. To prevent the limitation of the gray world algorithm, i.e. when a local color block contains a sky or a monochromatic part, the average reflectivity of the local color block should be different from the value 0.5 we set in the equation, in this case t_minTo be estimated erroneously, in order to obtain a global optimum result against over-enhancement and over-saturation, a global stretching strategy using the golden section method in the one-dimensional search algorithm is designed, which determines t using information of the entire image instead of using pixels_minThus, the IDE module provides a global stretch strategy for the golden section method in a one-dimensional search algorithm to determine t_min。

The IDE module provides a global stretching strategy of golden section method in one-dimensional search algorithm to determine t_minReferring to equation (14):

in the above formula (14), argmin {. is a minimum function; Ψ (-) is a saturation operator function; epsilon is a preset defogging result saturation; SF (-) is the scene reflectivity recovery formula;

a refers to the atmospheric light value of the original image I;

calculating by formula (4);

↓^δis the down-sampling operator with coefficient delta, (I) ↓^δThe method comprises the steps of down-sampling a corresponding image or matrix I in brackets to 1/delta of the original resolution; -

Original image I: if the size of the original image I is I multiplied by j, the original image I is mathematically transformed in the image processing module to obtain a matrix of the original image I,

refer to the corresponding image or matrix in parentheses

Down-sampling to 1/delta of the original resolution,

wherein

Three color channels at R, G, B, which refer to a point with position coordinates (I, j) in the original image IThe local mean value on the track is,

calculated by formula (5);

p₁and p₂Are obtained by simulation of a formula (7);

obtaining t by the IDE module search_minThen, t is added_minSubstitution into

Then, a mathematical model corresponding to the defogged image I 'is obtained, and the defogged image I' is ρ (x, y).

In this embodiment, epsilon is a preset saturation of the defogging result, and 2% of pixel values are clipped in the shadow and highlight portions, that is, epsilon is set to 0.02; let δ be 4, then ↓^δIs the down-sampling operator with the coefficient of 4, (I) ↓^δ1/4, down-sampling the corresponding image or matrix in brackets to the original resolution;

refers to the down-sampling 1/4 of the corresponding image or matrix in brackets to the original resolution.

The IDE module of this embodiment processes the original image I according to t by the above steps_minThe calculation formula (14) and the scene reflectivity rho (x, y) recovery formula (13) are fitted to obtain a mathematical model corresponding to the defogged image I ', the defogged image I' is rho (x, y), and the mathematical model corresponding to the defogged image of the original image I is obtained, so that the defogged image of the original image I is obtained and is output by the image output module.

Example six:

in this embodiment, based on the fifth embodiment, referring to fig. 2, the method for defogging and exposing an image based on an enhanced atmospheric scattering model specifically includes the following steps:

firstly, an original image I is input through an image input module, and then an image processing moduleThe block defoggs the original image I, and finally outputs a defogged image I' corresponding to the original image I through an image output module; the image processing module receives an original image I, the EASM module in the image processing module performs mathematical transformation on the original image I, and then the IDE module provides a global stretching strategy of a golden section method in a one-dimensional search algorithm to determine t_min(ii) a And finally, taking the mathematical model after mathematical treatment as the mathematical model of the defogged image.

The method comprises the following steps:

step 1, setting an original image I, wherein the original image I refers to image information obtained by outdoor scene information acquisition under a haze weather condition or a worse severe weather condition, and the original image I is not limited to a foggy image;

step 2, inputting the original image I into an image processing module, and firstly, carrying out mathematical transformation on the original image I by an EASM module, specifically:

the original image I is represented as:

In the above formula (1), (x, y) denotes a spatial position coordinate of the original image I, α (x, y) denotes a light absorption coefficient of a point whose position coordinate is (x, y), a denotes an atmospheric light value, ρ (x, y) denotes a scene reflectance of a point whose position coordinate is (x, y) in a fog-free scene, and t (x, y) denotes an atmospheric transmittance of a point whose position coordinate is (x, y), where when particles suspended in the atmosphere are uniformly distributed over the space, t (x, y) denotes e^{-β·d(x，y)}Where β represents a scattering coefficient (also referred to as a fog concentration influence coefficient), and d (x, y) represents a scene depth of a point whose position coordinate is (x, y).

Let the above-mentioned

Wherein t is_minIs the minimum value of t (x, y), and t (x, y) represents the atmospheric transmittance of the point with the position coordinate (x, y) in the original image I, and when the particles suspended in the atmosphere are uniformly distributed in the space, t (x, y) ═ e^{-β·d(x，y)}Wherein, in the step (A),beta represents the scattering coefficient, d (x, y) represents the scene depth for a point with position coordinates (x, y), t_min＝e^-β·max(d)；

According to the above

Converting equation (1) to equation (2):

step 3, in the formula (2), the atmospheric light value a of the original image I is obtained by the following method: calculating the original image I by a He method to obtain a dark channel image corresponding to the original image I, and then processing the dark channel image to obtain an atmospheric light value;

wherein Ω (x, y) is a set of pixels in the neighborhood centered on the coordinates (x, y),c is the color channel index, J^R、J^G、J^BRefers to three color channels of a fog-free image expressed using RGB; assuming a dark channel prior model, the dark channel pixel values of the fog-free image are usually small, substantially approaching 0, but the dark channel pixel values of the fog-containing image are substantially close to the fog density values, making it easier to calculate the atmospheric light values.

As described above, the atmospheric light value a of the original image I is obtained by: firstly, obtaining a dark channel image of an original image I according to a He method; then, dividing the dark channel image into pixel points, wherein each pixel point has a corresponding pixel value; then arranging all pixel points in the dark channel image according to the sequence of pixel values from large to small; finally, selecting pixel points with pixel values in a certain proportion in front, setting n selected pixel points, corresponding the spatial position coordinates of the pixel points in the dark channel image to the original image I to obtain the pixel points of the corresponding spatial position coordinates in the original image I, and setting the pixel values of the pixel points in R, G, B three color channels as X^R、X^G、X^BThen the atmospheric light value of the original image I in the R color channel

Atmospheric light value of original image I in G color channel

Atmospheric light value of original image I in B color channel

Thus obtaining the atmospheric light value of the original image I

Step 4, converting the formula (2) into a formula (3):

in the above-mentioned formula (3),

in the step 5, the step of the method is that,

is the average of the atmospheric light values of the original image I in the three color channels (R, G, B were chosen),

calculated by equation (4):

according to the method, firstly, a dark channel image of an original image I is obtained according to a He method; then, dividing the dark channel image into pixel points, wherein each pixel point has a corresponding pixel value; then arranging all pixel points in the dark channel image according to the sequence of pixel values from large to small, selecting pixel points with pixel values in a certain proportion in front, setting n selected pixel points, and corresponding the spatial positions of the pixel points in the dark channel image to the original image I; finally, obtaining pixel points of the space position corresponding to the original image I, and setting each pixel point at R, G, B three color channelsAre respectively X^R、X^G、X^BThen the atmospheric light value of the original image I in the R color channel

Atmospheric light value of original image I on G color channel

Atmospheric light value of original image I in B color channel

Thus obtaining

Namely, it is

Step 6, (x, y) refers to the position coordinates of the points in the original image I,

calculated by the formula (5),

in the above formula (5), Ω (x, y) is a set of points in the neighborhood centered on coordinates (x, y), | Ω (x, y) | is the total number of points in the set Ω (x, y), c is the color channel index, and c ∈ { R, G, B } refers to R, G being selectedB three color channels, I^R(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values on the R color channel of a point with position coordinates (x ', y'), I^G(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values on the G color channel of a point with position coordinates (x ', y'), I^B(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values of a point with a position coordinate (x ', y') on the B color channel, and the component value of the point with a position coordinate (x ', y') on the R color channel, the component value of the point with a position coordinate (x ', y') on the G color channel, and the component value of the point with a position coordinate (x ', y') on the B color channel are obtained by direct software acquisition, specifically by MATLAB software acquisition.

Step 7, in the above formula (3), (x, y) refers to the position coordinates of the midpoint in the original image I,

in the above equation (6), Ω (x, y) is a set of points in the neighborhood centered on coordinates (x, y), | Ω (x, y) | is the total number of points in the set Ω (x, y), C is a color channel index, C ∈ { R, G, B } means that R, G, B three color channels are selected, ρ ∈ { R, G, B }, and^R(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values, ρ, of a point with position coordinate (x ', y') on the R color channel in a fog-free scene^G(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values on the G color channel of a point with position coordinate (x ', y') in the fog-free scene, ρ^B(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values of a point with a position coordinate (x ', y') in a fog-free scene on the B color channel, and the point with a position coordinate (x ', y') in the R color channel in the fog-free sceneThe component values on the color channel, the component value of the point with the position coordinate (x ', y') on the G color channel in the fog-free scene, and the component value of the point with the position coordinate (x ', y') on the B color channel in the fog-free scene are obtained by directly obtaining through software, specifically by obtaining through MATLAB software.

According to MATLAB simulation calculation to obtain

In this embodiment, let

Step 8, fitting a logarithmic function by using a rational function according to the formula (7):

step 9, according to

And formula (7) substituted into formula (3), the formula (3) is converted into a formula

Formula (8):

continuing to convert equation (8) to equation (9):

λ₁·(t(x，y))²+λ₂(x，y)·t(x，y)+λ₃(x, y) ═ 0 equation (9)

In the above-mentioned formula (9),

to avoid assuming that the atmospheric reflectivity is constant, resulting in image depth discontinuities in some cases, a guided filter is used, with which equation (10) can be converted to equation (11):

in the above equation (11), F (·) represents a pilot filter operator;

equation (11) is simplified to equation (12):

in the formula (12), φ (-) is a calculation formula of the atmospheric transmittance of a point having a position coordinate of (x, y), ln (t)_min)，

p₁，p₂Of the five variables, the number of variables,

calculating by formula (4);

p₁，p₂Of the 7 variables:

wherein A refers to the atmospheric light value of the original image I, and the atmospheric light value A of the original image I is obtained by the following method: firstly, obtaining a dark channel image of an original image I according to a He method; then, dividing the dark channel image into pixel points, wherein each pixel point has a corresponding pixel value; then arranging all pixel points in the dark channel image according to the sequence of pixel values from large to small; finally, selecting pixel points with pixel values in a certain proportion in front, setting n selected pixel points, corresponding the spatial position coordinates of the pixel points in the dark channel image to the original image I to obtain the pixel points of the corresponding spatial position coordinates in the original image I, and setting the pixel values of the pixel points in R, G, B three color channels as X^R、X^c、X^BThen the atmospheric light value of the original image I in the R color channel

Atmospheric light value of original image I in G color channel

Atmospheric light value of original image I in B color channel

Thus obtaining the atmospheric light value of the original image I

can be obtained by calculation of formula (4);

can be obtained by calculation of formula (5);

parameter p₁And p₂Can be obtained by fitting of formula (7);

the original image I is processed through the above method steps to obtain a mathematical model I ═ ρ (x, y) of the corresponding defogged image.

Example seven:

in this embodiment, an image defogging and exposure method based on an enhanced atmospheric scattering model further includes, based on the sixth embodiment, with reference to fig. 2, the following steps:

wherein the image processing moduleThe block also comprises an IDE module, wherein the IDE module is used for further processing the processing result of the EASM model to obtain a mathematical model corresponding to the defogged image of the original image I, so as to obtain the defogged image of the original image I. To prevent the limitation of the gray world algorithm, i.e. when a local color block contains a sky or a monochromatic part, the average reflectivity of the local color block should be different from the value 0.5 we set in the equation, in this case t_minTo be estimated erroneously, in order to obtain a global optimum result against over-enhancement and over-saturation, a global stretching strategy using the golden section method in the one-dimensional search algorithm is designed, which determines t using information of the entire image instead of using pixels_minThus, the IDE module provides a global stretch strategy for the golden section method in a one-dimensional search algorithm to determine t_min。

in the above formula (14), argmin {. is a minimum function; Ψ (-) is a saturation operator function; epsilon is a preset defogging result saturation; SF (-) is the scene reflectivity recovery formula; a refers to the atmospheric light value of the original image I;

↓^δis the down-sampling operator with coefficient delta, (I) ↓^δThe method is that I is reduced and sampled to 1/delta of the original resolution for the image or the matrix corresponding to the parenthesis, and an original image I is set as follows: if the size of the original image I is I multiplied by j, the original image I is mathematically transformed in the image processing module to obtain a matrix of the original image I,

wherein [ R ] is_i，j，G_i，j，B_i，j]Refers to the component values of the point with position coordinate (I, j) in the original image I on R, G, B color channels, respectively, and the point in the original image I is R, G, B color channelThe component values on the three color channels can be directly obtained through software, and in the embodiment, the component values are directly obtained through MATLAB software;

refer to the corresponding image or matrix in parentheses

Down-sampling to 1/delta of the original resolution,

wherein

Refers to the local mean value of the point with position coordinates (I, j) in the original image I on R, G, B three color channels,

calculated by formula (5);

calculating by formula (4); p is a radical of₁And p₂Are obtained by simulation of a formula (7);

obtaining t by the IDE module search_minAfter that, combine

Then obtain the mathematics corresponding to the defogged image IModel, defogged image I ═ ρ (x, y).

In this embodiment, p₁And p₂Are all obtained by simulation of formula (7), p₁0.397 and p₂＝0.07747；

To sum up, t can be calculated by the IDE module in this embodiment_min；

Example eight:

an image defogging and exposure method based on the enhanced atmospheric scattering model of the embodiment is based on the seventh embodiment,

the atmospheric light value A of the original image I is obtained by the following method: firstly, obtaining a dark channel image of an original image I according to a He method; then, dividing the dark channel image into pixel points, wherein each pixel point has a corresponding pixel value; then arranging all pixel points in the dark channel image according to the sequence of pixel values from large to small; finally, selecting a certain proportion of pixel points with the pixel values in front, in the embodiment, selecting pixel points with the pixel values in front 0.1%, setting n selected pixel points, and corresponding the spatial position coordinates of the pixel points in the dark channel image to the original image I to obtain the original image ISetting the pixel values of the corresponding pixel points of the spatial position coordinates at R, G, B three color channels as X^R、X^G、X^BThen the atmospheric light value of the original image I in the R color channel

Atmospheric light value of original image I in G color channel

Atmospheric light value of original image I in B color channel

Thus obtaining the atmospheric light value of the original image I

Calculated by equation (4):

according to the method, firstly, a dark channel image of an original image I is obtained according to a He method; then, dividing the dark channel image into pixel points, wherein each pixel point has a corresponding pixel value; then all pixel points in the dark channel image are arranged according to the sequence of the pixel values from large to small, and certain pixel value in front is selectedIn the present embodiment, pixel points with a pixel value of 0.1% in the first time are selected as proportional pixel points, n selected pixel points are set, and the spatial positions of the pixel points in the dark channel image are mapped to the original image I; finally, obtaining pixel points of the space position corresponding to the original image I, and setting the pixel values of the pixel points on R, G, B three color channels as X respectively^R、X^G、X^BThen the atmospheric light value of the original image I in the R color channel

Atmospheric light value of original image I on G color channel

Atmospheric light value of original image I in B color channel

Thus obtaining

Namely, it is

Example nine:

in an image defogging and exposure method based on an enhanced atmospheric scattering model according to an eighth embodiment, an original image I is shown in fig. 3, where fig. 3(a) and 3(b) are both fog-free images, and fig. 3(c), 3(d) and 3(e) are both fog-containing images. As can be seen from the above, the dark channel image is calculated according to the He method for the original image I, and the atmospheric light value is estimated through the dark channel prior model, so that the He method is firstly used to process the image I in fig. 3, so as to obtain the dark channel image map 4 corresponding to the image I in fig. 3, the processing result is shown in fig. 4, the dark channel image corresponding to the image I in fig. 3(a) is the image 4(a), the dark channel image corresponding to the image I in fig. 3(b) is the image 4(b), the dark channel image corresponding to the image I in fig. 3(c) is the image 4(c), the dark channel image map 4(b) corresponding to the image I in fig. 3(a) is relatively dark, and the dark channel image 4(c) corresponding to the image I in fig. 3(c) and the dark channel image 4(d) corresponding to the image I in fig. 3(e) and the image 4(e) in fig. 3(c) corresponding to the image 4(e) (e) The images of all dark channels are brighter, and the pixel values are close to the fog concentration.

In this embodiment, an HL method, an IDGCP method, a DehazeNet method, an MSCNN method, and a method (IDE) of the present invention are respectively used to perform defogging processing on an original image I corresponding to fig. 3, fig. 5 is a defogging effect diagram obtained after the defogging processing of fig. 3 is performed by using the HL method, in fig. 5, a defogging image corresponding to fig. 3(a) is fig. 5(a), a defogging image corresponding to fig. 3(b) is fig. 5(b), a defogging image corresponding to fig. 3(c) is fig. 5(c), a defogging image corresponding to fig. 3(d) is fig. 5(d), and a defogging image corresponding to fig. 3(e) is fig. 5 (e); fig. 6 is a diagram of the defogging effect obtained after the defogging process of fig. 3 is performed by using the IDGCP method, the defogged image corresponding to fig. 3(a) is fig. 6(a), the defogged image corresponding to fig. 3(b) is fig. 6(b), the defogged image corresponding to fig. 3(c) is fig. 6(c), the defogged image corresponding to fig. 3(d) is fig. 6(d), and the defogged image corresponding to fig. 3(e) is fig. 6 (e); fig. 7 is a diagram of the defogging effect obtained after the defogging process of fig. 3 is performed by using the DehazeNet method, the defogging image corresponding to fig. 3(a) is fig. 7(a), the defogging image corresponding to fig. 3(b) is fig. 7(b), the defogging image corresponding to fig. 3(c) is fig. 7(c), the defogging image corresponding to fig. 3(d) is fig. 7(d), and the defogging image corresponding to fig. 3(e) is fig. 7 (e); fig. 8 is a defogging effect diagram obtained after the defogging process of fig. 3 is performed by using the MSCNN method, the defogging image corresponding to fig. 3(a) is fig. 8(a), the defogging image corresponding to fig. 3(b) is fig. 8(b), the defogging image corresponding to fig. 3(c) is fig. 8(c), the defogging image corresponding to fig. 3(d) is fig. 8(d), and the defogging image corresponding to fig. 3(e) is fig. 8 (e); fig. 9 is a graph of the defogging effect obtained after the defogging process of fig. 3 is performed by using the method of the present invention, the defogged image corresponding to fig. 3(a) is fig. 9(a), the defogged image corresponding to fig. 3(b) is fig. 9(b), the defogged image corresponding to fig. 3(c) is fig. 9(c), the defogged image corresponding to fig. 3(d) is fig. 9(d), and the defogged image corresponding to fig. 3(e) is fig. 9 (e). It can be seen that the present invention has better effect on image defogging than the other 4 types, and has smaller running expense.

The HL method, IDGCP method, DehazeNet method, MSCNN method and the method of the Invention (IDE) were compared below using a qualitative method.

Table 1 shows that the HL method, the IDGCP method, the DehazeNet method, the MSCNN method and the invention method (IDE)5 methods are used for quantitative comparison of the defogging effect of the Image in FIG. 3, and three common indicators are adopted, namely the mean ratio of the gradients at the visible edge (R), a Natural Image Quality Evaluator (NIQE) and a Fog Density Evaluator (FADE), wherein the larger the R is, the more abundant the information contained in the Image is; the smaller the NIQE is, the truer and more natural the restored image is; the smaller FADE indicates less haze remaining in the defogged picture. It can thus be seen that the inventive method works better by comparing the defogging effect with the remaining 4 methods, and the quantitative comparison visual chart of the defogging effect in table 1 refers to fig. 10, where fig. 10(a) is a comparison of the 5 methods for the operating time of the defogging process of fig. 3(e) by the image size s of 400 times the 400 size, fig. 10(b) is a comparison of the performance indicators of the defogging process R of fig. 3(e) with the 5 methods, fig. 10(c) is a comparison of the performance indicators of the defogging process NIQE of fig. 3(e) with the 5 methods, and fig. 10(d) is a comparison of the performance indicators of the defogging process FADE of fig. 3(e) with the 5 methods.

Tables 2 and 3 show that the HL method, IDGCP method, DehazeNet method, MSCNN method and method of the Invention (IDE)5 methods process fig. 3 comparing the time required for different and same resolutions respectively, tables 2 show the HL method, IDGCP method, and tables 3 show the DehazeNet method, MSCNN method and method of the invention, by comparing with the remaining 4 methods, the defogging speed of the invention has a great advantage, and experiments show that, compared with ASM, EASM has a better modeling capability for blurred images, and the final IDE is superior to most of the latest technologies in terms of processing efficiency and recovery quality.

TABLE 1

TABLE 3

Example ten:

in the embodiment, based on the ninth embodiment, the simulation language is matlab (R2016b), the operating environment is Windows 10, and the computer is configured as intel (R) core (tm) i5-7200UCPU @2.50GHz 16GB RAM.

The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims

1. An image defogging and exposure method based on an enhanced atmospheric scattering model is characterized by comprising the following steps: the method provides an enhanced atmospheric scattering model-based image defogging system comprising the following modules:

an image input module: for reading an original image I;

the method comprises the following steps:

s1, firstly, reading an original image I through an image input module;

s2, defogging the original image I through the image processing module;

2. The enhanced atmospheric scattering model-based image defogging and exposure method according to claim 1, wherein: the original image I is not limited to only a foggy image.

3. The enhanced atmospheric scattering model-based image defogging and exposure method according to claim 2, wherein: the image processing module includes an EASM module that provides an EASM model whose expression is as follows:

4. The enhanced atmospheric scattering model-based image defogging and exposure method according to claim 3, wherein:

order to

Wherein t is_minIs the minimum value of t (x, y);

then the above equation (1) etc. is changed to equation (2):

5. The enhanced atmospheric scattering model-based image defogging and exposure method according to claim 4, wherein:

converting equation (2) to equation (3):

in the above-mentioned formula (3),

in the above formula (5), Ω (x, y) is a set of points in the neighborhood centered on coordinates (x, y), | Ω (x, y) | is the total number of points in the set Ω (x, y), c is a color channel index, and c ∈ { R, G, B } refers to R, G, B three colors selectedColor channel, I^R(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values on the R color channel of a point with position coordinates (x ', y'), I^G(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values on the G color channel of a point with position coordinates (x ', y'), I^B(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values of a point with a position coordinate of (x ', y') on the B color channel, and a component value of the point with the position coordinate of (x ', y') on the R color channel, a component value of the point with the position coordinate of (x ', y') on the G color channel, and a component value of the point with the position coordinate of (x ', y') on the B color channel are directly obtained through software;

in the above formula (6), Ω (x, y) is a set of points in the neighborhood centered on coordinates (x, y), | Ω (x, y) | is the total number of points in the set Ω (x, y), c is a color channel index, c ∈ { R, G, B } refers to R, G, B three color channels being selected, ρ ∈ { R, G, B }, and^R(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values, ρ, of a point with position coordinate (x ', y') on the R color channel in a fog-free scene^G(x ', y'), (x ', y') ∈ Ω (x, y) refer to the component values on the G color channel of a point with position coordinate (x ', y') in the fog-free scene, ρ^B(x ', y'), (x ', y') ∈ Ω (x, y) refer to component values of a point with a position coordinate (x ', y') on the B color channel in a fog-free scene, component values of a point with a position coordinate (x ', y') on the R color channel in a fog-free scene,y ') and the component value of the point with the position coordinate (x ', y ') on the B color channel under the fog-free scene are obtained by directly acquiring through software.

6. The enhanced atmospheric scattering model-based image defogging and exposure method according to claim 5, wherein:

order to

in the above formula (7), p₁And p₂Are all parameters of rational functions;

according to

And formula (7) converting the formula (3) into formula (8):

converting equation (8) and the like to equation (9):

λ₁·(t(x，y))²+λ₂(x，y)·t(x，y)+λ₃(x, y) ═ 0 equation (9)

In the above-mentioned formula (9),

then, using a guided filter, equation (10) is converted to equation (11):

in the above equation (11), F (·) represents a pilot filter operator;

equation (11) is simplified to equation (12):

in the above-mentioned formula (12),

calculating by formula (4);

calculating by formula (4);

7. The enhanced atmospheric scattering model-based image defogging and exposure method according to claim 6, wherein: the image processing module also comprises an IDE module which provides a global stretching strategy calculation t of a golden section method in a one-dimensional search algorithm_minReferring to equation (14):

in the above formula (14), argmin {. is a minimum function; Ψ (-) is a saturation operator function; epsilon is a preset defogging result saturation; SF (-) is the scene reflectivity recovery formula; a refers to the atmospheric light value of the original image I; ↓^δIs the down-sampling operator with coefficient delta, (I) ↓^δAnd

8. The enhanced atmospheric scattering model-based image defogging and exposure method according to claim 7, wherein: t obtained from the formula (14)_minThe mathematical model corresponding to the defogged image I 'is obtained by substituting the formula (13) ρ (x, y), and the defogged image I' is ρ (x, y).

9. The enhanced atmospheric scattering model-based image defogging and exposure method according to claim 8, wherein: let δ be 4 and let e be 0.02.

10. The enhanced atmospheric scattering model-based image defogging and exposure method according to claim 9, wherein: the S2 includes the steps of:

s21, firstly, the image processing module receives an original image I;