CN115861104B - Remote sensing image defogging method based on transmissivity refinement - Google Patents
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Abstract
The invention provides a defogging method for a remote sensing image based on transmissivity refinement, which is used for solving the problems of inaccurate transmissivity estimation in the remote sensing image and low defogging quality of the remote sensing image; and the contrast ratio of the defogged remote sensing image is low. The implementation steps of the invention are as follows: calculating the atmospheric intensity value of the foggy remote sensing image; a Gaussian weighted dark channel image of the foggy remote sensing image is obtained by using a Gaussian weighting algorithm; calculating the refined transmissivity of the foggy remote sensing image; and defogging the foggy remote sensing image according to the atmospheric intensity value and the refined transmissivity to obtain a defogged image. The invention solves the problem of inaccurate transmissivity calculation in the prior art, can defog foggy images comprising remote sensing images with high quality, and can improve the contrast of the images and increase the detail information of the images after defogging.
Description
Technical Field
The invention belongs to the technical field of image processing, and further relates to a remote sensing image defogging method based on transmissivity refinement in the technical field of remote sensing image processing. The invention can reduce the distortion degree of the remote sensing image and the visibility of objects in the remote sensing image by eliminating fog and cloud in the remote sensing image, thereby being convenient for the segmentation of the subsequent remote sensing image and the target detection and identification of application scenes.
Background
When in air, the acquired remote sensing image typically has low contrast and poor visibility, which not only reduces the visual effect, but also prevents subsequent processing in a computer vision system. In response to this problem, a number of image defogging methods and techniques have been proposed for restoring remote sensing images. Common methods include a series of derived algorithms represented by the Retinex algorithm, histogram equalization, wavelet transformation and the like, but the methods are easy to cause detail information loss and noise addition when defogging images, and distortion problems such as oversaturation, serious loss of image detail information and the like occur. With the development of deep learning, many defogging methods based on various neural networks have been proposed. However, image defogging is a step in image preprocessing and is often not the final desired result, and it takes a lot of time to train the defogging neural network model is wasteful.
A remote sensing image defogging method based on a single atmospheric scattering model is disclosed in a patent document of a foggy remote sensing image restoration method (application number: 2019107303597, application publication number: CN 110490821A) of a national academy of sciences of China, vinca optical precision machinery and physical institute. Firstly, on the basis of a single atmospheric scattering model, a remote sensing image degradation model based on active illumination of an atmospheric micro point light source is established, and a halation component of an original foggy remote sensing image is obtained through the remote sensing image degradation model; secondly, removing the atmospheric multi-scattering effect and the non-uniformity of the original foggy remote sensing image according to the halation component, and dividing the original foggy remote sensing image into a halation image and a haze image after halation removal; thirdly, performing transmittance and atmospheric light estimation on the haze image after removing the halation by adopting a single atmospheric scattering model, and performing defogging restoration processing on the haze image after removing the halation by adopting a dark channel prior image defogging algorithm to obtain a defogging restoration image. The defogging method for the remote sensing image is simple in implementation. However, the method still has the defects that the obtained transmissivity value is inaccurate and the defogging quality of the remote sensing image is poor because the transmissivity is not refined.
Disclosure of Invention
The invention aims to provide a remote sensing image defogging method based on transmissivity refinement, aiming at overcoming the defects of the prior art, and aims to solve the problems that the transmissivity refinement is not carried out and defogging quality of the remote sensing image is low in the prior art.
The technical idea for realizing the purpose of the invention is as follows: according to the invention, the Gaussian weighted dark channel image of the remote sensing image is obtained by using the Gaussian weighting algorithm, so that the transmissivity of the image is thinned, and the problem of inaccurate transmissivity value is solved. A Gaussian weighting algorithm is used for obtaining a finer transmission diagram, so that finer transmissivity can be obtained, and the problem of low defogging quality of a remote sensing image is solved.
In order to achieve the above purpose, the technical scheme adopted by the invention comprises the following steps:
step 1, calculating an atmospheric intensity value A of a foggy remote sensing image I:
step 2, a Gaussian weighted dark channel image I' of the foggy remote sensing image I is obtained by using a Gaussian weighting algorithm:
step 2.1, calculating a dark channel image I of the foggy remote sensing image I according to the following formula 1 ：
wherein ,the minimum value in three channels R, G and B is calculated, and R, G and B respectively represent three channels corresponding to red, green and blue pixels in the foggy remote sensing image;
step 2.2, utilize I 2 ＝dilate(I 1 -erode(I 1 ) Formula (iv)Obtaining a dark channel image I 1 Edge image I of (2) 2 Wherein, the dialate represents a morphological dilation operation, and the erode represents a morphological erosion operation;
step 2.3, utilize I 3 ＝0.5I 2 +0.5I 1 Formula, calculating compensation dark channel image I of foggy remote sensing image I 3 ；
Step 2.4, calculating a Gaussian weighted dark channel image I' of the foggy remote sensing image I according to the following formula:
wherein ,e{· -represents an exponential operation with a base of natural constant e;
step 3, calculating the refined transmissivity T of the foggy remote sensing image I by using a formula of T=255-I';
step 4, defogging the foggy remote sensing image I according to the atmospheric intensity value A and the refined transmissivity T to obtain a defogged image R:
wherein F represents a center-surround function,a (1-T) represents an atmospheric light attenuating portion,>representing a convolution operation.
Compared with the prior art, the invention has the following advantages:
the Gaussian weighted dark channel image of the foggy remote sensing image is obtained through an image Gaussian weighted fusion algorithm, the refined transmissivity of the foggy remote sensing image is calculated, the problem of inaccurate transmissivity calculation in the prior art is solved, and the defogging quality of the remote sensing image is improved.
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FIG. 1 is a flow chart of the present invention;
fig. 2 is a simulation diagram of the present invention, in which fig. 2 (a) is an original image containing fog, fig. 2 (b) is an image after defogging of the original image 2 (a) by a method of the related art, and fig. 2 (c) is an image after defogging of the original image 2 (a) by a method of the present invention;
Detailed Description
The invention is described in further detail below with reference to the drawings and the specific examples.
Referring to fig. 1, the present invention includes the steps of:
and step 1, acquiring an atmospheric intensity value A of the foggy image I.
Step 1.1, the brightness of pixels in an image is between 0 and 255, the brightness of pixels close to 255 is high, and the brightness close to 0 is low; ordering all pixels in the foggy remote sensing image I with the size of L multiplied by W from large to small according to brightness to form a brightness pixel set N, wherein L is more than or equal to 512, and W is more than or equal to 512; selecting the first 0.1% of pixels from the brightness pixel set N to form the brightness pixel set N 1 ；
Step 1.2, at luminance pixel set N 1 The brightness value of the first pixel is B 1 The brightness value of the second pixel is B 2 The brightness value of the third pixel is B 3 By analogy, the luminance value of the ith pixel is wherein ,/>Representing a rounding down operation; collecting luminance pixels N 1 The brightness values of all pixels in the array are summed to obtain a brightness value sum B sum ，/>Wherein Σ represents the summing operation;
step 1.3, computing a luminance pixel set N 1 The average value of the brightness values of all pixels in the image is used for obtaining the atmospheric intensity value of the foggy remote sensing image IA，
And 2, obtaining a Gaussian weighted dark channel image I' of the foggy remote sensing image I by using a Gaussian weighting algorithm.
Step 2.1, the dark channel prior principle is based on observation of a large number of outdoor haze-free images, and the dark channel prior assumption considers that in most of non-sky local areas, the value of one color channel of some pixels tends to be 0; obtaining a dark channel image I of the foggy remote sensing image I according to the dark channel priori principle 1 Can be expressed as wherein ,/>The minimum value in three channels R, G and B is calculated, and R, G and B respectively represent three channels corresponding to red, green and blue pixels in the foggy remote sensing image;
step 2.2, utilize I 2 ＝dilate(I 1 -erode(I 1 ) Formula, obtain dark channel image I 1 Edge image I of (2) 2 Wherein, the dialate represents a morphological dilation operation, and the erode represents a morphological erosion operation;
the corrosion operation of the image can eliminate boundary points of the image, so that the image is contracted inwards along the boundary, and parts smaller than the designated structural body elements can be removed, so that noise in the image can be effectively eliminated; the expansion operation of the image is opposite to the corrosion operation, the expansion operation can expand the boundary of the image, and the boundary information of the image can be accurately extracted; the method comprises the steps of firstly corroding and then expanding to form morphological open operation, so that the image noise can be effectively eliminated, and meanwhile, the edge information of the image can be accurately positioned;
step 2.3, utilize I 3 ＝0.5I 2 +0.5I 1 Formula, calculating compensation dark channel image I of foggy remote sensing image I 3 ；
The image weighted fusion operation can enlarge the content of the imageSome time space information reduces uncertainty and increases reliability; edge image I 2 And dark channel image I 1 The dark channel pixels at the edges of the image can be effectively compensated by weighting and fusion;
step 2.4, calculating a Gaussian weighted dark channel image I' of the foggy remote sensing image I according to the following formula:
wherein ,e{· -represents an exponential operation with a base of natural constant e;
the gaussian weighting function is a process of performing weighted average on the whole image, and the value of each pixel point is obtained by performing weighted average on the value of each pixel point and other pixel values in the neighborhood. The Gaussian weighting function can be used for effectively refining the pixel boundary of the dark channel image, so that an image with finer pixel edges is obtained.
Step 3, calculating the refined transmissivity T of the foggy remote sensing image I by using a formula of T=255-I';
and 4, obtaining a defogging image R of the foggy remote sensing image I.
Step 4.1, an atmospheric scattering model table is I=Jt+A (1-t), wherein I represents a foggy remote sensing image, J represents a clear remote sensing image without fogs, t represents transmissivity, and A represents an atmospheric intensity value; the Retinex theoretical model is denoted s=rl, where S represents a clear remote sensing image without fog, R represents a reflected component, L represents an illuminance component, where, representing a convolution operation, F representing a center-surround function, < ->
Step 4.2, fusing the atmospheric scattering model and the Retinex theoretical model, and setting J=S, then fusingCombined defogging model
Step 4.2, defogging the foggy remote sensing image I according to the fused defogging model and combining the atmospheric intensity value A and the refined transmissivity T to obtain a defogging image R:
wherein F represents a center-surround function,a (1-T) represents an atmospheric light attenuating portion,>representing a convolution operation.
The effects of the present invention are further described below in conjunction with simulation experiments:
1. simulation experiment conditions:
the hardware platform of the simulation experiment of the invention is: the processor is Intel i5-10400F, the main frequency is 2.9GHz, and 16G runs the memory.
The software platform of the simulation experiment platform is as follows: windows 11 operating system and Visual Studio2017 software.
The images used in the simulation experiments of the present invention were from paper "A remote sensing image dataset for cloud removal" (Computer Vision and Pattern Recognition, vol. Abs/1901.00600).
2. Simulation content and result analysis:
the simulation experiment of the invention adopts the invention and a prior art (a new unmanned aerial vehicle remote sensing image defogging method) to respectively defogging a foggy remote sensing image, and the result is shown in figure 2.
In simulation experiments, one prior art technique employed refers to:
a novel unmanned aerial vehicle remote sensing image defogging method is proposed by Tao Gao et al in published paper "A novel UAV Sensing Image Defogging Method" (IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2020, pp.2610-2625).
The simulation effect of the present invention is further described below with reference to fig. 2.
Fig. 2 (a) is an original image containing fog, fig. 2 (b) is an image of the original image after defogging of fig. 2 (a) by a method of the related art, and fig. 2 (c) is an image of the original image 2 (a) after defogging by a method of the present invention.
As can be seen from fig. 2 (b), residual fog still exists in the image after defogging by the existing method, and the edge of the river is blurred in fig. 2 (b), and the ground object is not clearly displayed, so that defogging quality of the existing method is low, and detail recovery is poor. Compared with the prior art. Fig. 2 (c) thoroughly eliminates residual fog at the depth-of-field abrupt change, suppresses color cast phenomenon, and simultaneously can retain detailed information of ground objects, so that the defogging effect is more prominent. Therefore, the defogging effect of fig. 2 (c) is good, the overall color is natural, haze in the image can be removed well, and the image contrast and saturation are improved.
Comparing the invention with the classical method disclosed in the prior art, the comparison method comprises the following steps: DCP, CAP and MSBDN; the adopted evaluation index is Structural Similarity (SSIM), the range of the SSIM is 0-1, and the larger the value is, the higher the defogging image quality is. For fig. 2 (a), the DCP process has an SSIM of 0.405, the cap process has an SSIM of 0.774, the msbdn process has an SSIM of 0.647, and the inventive process has an SSIM of 0.873. Therefore, the defogging remote sensing image obtained by the defogging method has the advantages of natural overall color, higher contrast ratio and higher defogging quality.
The simulation experiment results show that the invention has good defogging capability for remote sensing images containing fog; the invention can accurately recover the image and has high defogging quality.
Claims (2)
1. A defogging method of a remote sensing image based on transmissivity refinement is characterized in that a Gaussian weighted dark channel image of a foggy remote sensing image is obtained by using a Gaussian weighting algorithm, and the refined transmissivity is calculated; the defogging method comprises the following steps:
step 1, calculating an atmospheric intensity value A of a foggy remote sensing image I:
step 2, a Gaussian weighted dark channel image I' of the foggy remote sensing image I is obtained by using a Gaussian weighting algorithm:
step 2.1, calculating a dark channel image I of the foggy remote sensing image I according to the following formula 1 ：
wherein ,the minimum value in three channels R, G and B is calculated, and R, G and B respectively represent three channels corresponding to red, green and blue pixels in the foggy remote sensing image;
step 2.2, utilize I 2 ＝dilate(I 1 -erode(I 1 ) Formula, obtain dark channel image I 1 Edge image I of (2) 2 Wherein, the dialate represents a morphological dilation operation, and the erode represents a morphological erosion operation;
step 2.3, utilize I 3 ＝0.5I 2 +0.5I 1 Formula, calculating compensation dark channel image I of foggy remote sensing image I 3 ；
Step 2.4, calculating a Gaussian weighted dark channel image I' of the foggy remote sensing image I according to the following formula:
wherein ,e{·} An exponential operation that bases on a natural constant e;
step 3, calculating the refined transmissivity T of the foggy remote sensing image I by using a formula of T=255-I';
step 4, defogging the foggy remote sensing image I according to the atmospheric intensity value A and the refined transmissivity T to obtain a defogged image R:
wherein F represents a center-surround function,a (1-T) represents an atmospheric light attenuating portion,>representing a convolution operation.
2. The defogging method for remote sensing images based on transmissivity refinement according to claim 1, wherein the atmospheric intensity value a of step 1 is obtained by:
the first step, the brightness of the pixels in the image is between 0 and 255, the brightness of the pixels close to 255 is high, and the brightness close to 0 is low; ordering all pixels in the foggy remote sensing image I with the size of L multiplied by W from large to small according to brightness to form a brightness pixel set N, wherein L is more than or equal to 512, and W is more than or equal to 512; selecting the first 0.1% of pixels from the brightness pixel set N to form the brightness pixel set N 1 ；
Second, in the luminance pixel set N 1 The brightness value of the first pixel is B 1 The brightness value of the second pixel is B 2 The brightness value of the third pixel is B 3 By analogy, the luminance value of the ith pixel is B i ， wherein ,/>Representing a rounding down operation; collecting luminance pixels N 1 The brightness values of all pixels in the array are summed to obtain a brightness value sum B sum ，/>Wherein Σ represents the summing operation;
third step, calculate the brightness pixel set N 1 The average value of the brightness values of all pixels in the image is used for obtaining the atmospheric intensity value A of the foggy remote sensing image I,
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