CN111476736A - Image defogging method, terminal and system - Google Patents
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Abstract
The invention discloses an image defogging method which comprises the steps of converting a foggy image from an RGB color space to an L AB color space to obtain a foggy image of a L AB color space, respectively carrying out wavelet transformation denoising processing on L color channels and A, B color channels of the foggy image of a L AB color space to obtain denoised L color channels and A, B color channels, carrying out dark primary color prior method processing on the L color channels according to transmittance distribution, combining the processed L color channels and the processed A, B color channels to obtain a L AB image, converting the L AB image into an RGB image, and outputting the defogged image.
Description
Technical Field
The invention relates to an image processing technology, in particular to an image defogging method, a terminal and a system.
Background
Modern unmanned aerial vehicles are more and more widely used, image information, video information and position information are mainly acquired through task equipment, and therefore post-processing of image data becomes more critical. In the actual process, unmanned aerial vehicle receives more atmospheric environment's influence because the shooting position is higher, especially images the effect not good under the foggy weather, has influenced the tracking of target and to the reconnaissance of topography etc.. Therefore, the defogging technology research aiming at the unmanned aerial vehicle image has great significance.
The image defogging technology at present mainly comprises two directions: respectively image enhancement and image restoration. The image enhancement defogging technology mainly improves the contrast of an image through algorithms and enhances the details of the image, and mainly comprises a defogging technology based on histogram equalization, a defogging algorithm based on Retinex theory and a defogging algorithm based on wavelet change.
The defogging technology for image restoration is to estimate the scene depth by establishing an atmospheric scattering model and remove the defogging influence by inverting the image imaging process. Of these, best is the dark channel first pass algorithm proposed by Khamming et al, where estimating the transmittance through minimum filtering, optimizing the transmittance using techniques such as soft matting or bilateral filtering can achieve better experimental results, but the algorithm is computationally complex.
Disclosure of Invention
The embodiment of the invention provides an image defogging method, terminal and system, and aims to solve the problem that an existing unmanned aerial vehicle ground information processing terminal cannot further process a foggy image.
In order to achieve the purpose, the technical scheme of the invention is as follows:
in a first aspect, an embodiment of the present invention provides an image defogging method, including:
converting the foggy image from the RGB color space to L AB color space to obtain a foggy image of L AB color space;
respectively carrying out wavelet transformation denoising processing on L and A, B color channels of the foggy image of L AB color space to obtain denoised L and A, B color channels;
calculating an atmospheric light value in a L color channel, selecting first 0.1% pixel points with the maximum brightness value from dark primaries obtained by calculation of a L color channel, calculating the transmittance by taking the maximum value of the pixel points corresponding to an original image as the value of A, and finally obtaining a new L color channel through recovery calculation
Merging the processed L and A, B color channels to obtain a L AB image;
further, the L AB image is converted into an RGB image and output for display.
In a second aspect, an embodiment of the present invention provides an image defogging system, including:
the color conversion module is used for converting the foggy image from the RGB color space to L AB color space to obtain a foggy image of L AB color space;
the wavelet transformation denoising processing module is used for respectively carrying out wavelet transformation denoising processing on L and A, B color channels of the foggy image of the L AB color space to obtain denoised L and A, B color channels;
and the dark primary color prior processing module is used for carrying out dark primary color prior method processing on the L color channel image according to the transmittance distribution and outputting a new L color channel image.
A merging module, configured to merge the processed L and A, B color channels to obtain a L AB image;
in a third aspect, an embodiment of the present invention provides an image defogging processing terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the image defogging method when executing the computer program.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned image defogging method.
Compared with the prior art, the invention has the beneficial effects that:
the method is based on the requirements of atomization and the need of clarification processing of the foggy aerial image, and overcomes the defect of high computation complexity of dark channel prior by using the advantage of more concentrated information of wavelet transformation based on color space transformation, so that the defogging of the aerial image under the condition of uneven low-altitude haze is not only quick, but also more thorough, and has lower computation complexity and higher operation speed than the classical dark channel prior algorithm.
Drawings
FIG. 1 is a flowchart of an image defogging method according to an embodiment of the present invention;
FIG. 2 is an original hazy image;
FIG. 3a is an L component image of a hazy image;
FIG. 3b is an A component image of a hazy image;
FIG. 3c is a B component image of a hazy image;
FIG. 3d is an L component histogram;
FIG. 3e is an A component histogram;
FIG. 3f is a B component histogram;
FIG. 4 is a final image after defogging;
FIG. 5 is a schematic diagram of an image defogging system according to an embodiment of the invention;
fig. 6 is a schematic composition diagram of an image defogging processing terminal according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and detailed description.
Example (b):
referring to fig. 1, the image defogging method provided by the embodiment includes the following steps:
101. the foggy image is converted from the RGB color space to the L AB color space as shown in fig. 2, obtaining a foggy image in L AB color space, and the foggy part can be better distinguished in L AB mode.
102. The three color channels L and A, B of the foggy image of L AB color space are respectively subjected to wavelet transformation denoising processing to obtain three color channels L and A, B after denoising, and details are sharpened by respectively performing wavelet transformation on the three color channels L, A and B, so that the contrast can be more finely adjusted, as shown in fig. 3a-3 f.
The wavelet transform has good locality in the time-frequency domain, and the variable scale characteristic of the wavelet transform enables the wavelet transform to have a 'concentration' capability on a determined signal. After wavelet transform of an image containing noise, the image noise and the signal noise show different characteristics: the energy of the signal is mainly concentrated on some bright lines, and most coefficients approach 0; the distribution of noise is opposite to that of signals, the coefficients of the noise are uniformly distributed in the whole scale space, the amplitude difference is not large (certain smoothing effect on the noise can be achieved under the large scale), and the characteristic provides a basis for image denoising based on wavelet transformation.
103. According to the transmittance distribution, L color channel images are processed by a dark primary color priori method, and new L color channel data are obtained.
104. The processed L and A, B color channels are combined to obtain a L AB image, and the image is converted into an RGB image, as shown in fig. 4.
Therefore, the method is based on the requirements of atomization and the need of clarification processing of aerial images in foggy days, and based on color space conversion, the advantage that information is more concentrated is overcome by utilizing wavelet transformation, the defect that the complexity of dark primary color prior calculation is high is overcome, and the aerial images in low altitude under the condition of uneven haze are defogged quickly and more thoroughly. Compared with the classic dark primary color prior defogging algorithm, the algorithm of the invention has the advantages of low calculation complexity and higher operation speed, and is reduced by about 1/3 in the running time.
The wavelet transform denoising method is based on the principle that the wavelet coefficient amplitude of a signal is larger than the coefficient amplitude of noise after wavelet decomposition, the specific processing procedure is that the signal containing noise is subjected to wavelet decomposition on each scale, the whole decomposition value under the large scale is reserved, a threshold value is set for the decomposition value under the small scale, the wavelet coefficient with the amplitude lower than the threshold value is set to be zero, and the wavelet coefficient higher than the threshold value is completely reserved, finally, the wavelet coefficient obtained after processing is reconstructed by inverse wavelet transform, and an effective signal is restored.
Specifically, the above-mentioned dark primary color prior processing on the L color channel image according to the transmittance distribution, and converting the format output defogged image includes:
according to the dark channel prior theory, for a fog-free image j (x), the dark channel prior law is:
in the formula, Jdark(x) Color values of the dark channel map of J (x); j. the design is a squarec(y) is the color value of one of the r, g, b channels in J (x); Ω (x) is a region centered at x; c represents any one color channel of r, g and b;
the transmission t (x) is calculated as:
after a dark channel check algorithm and transmittance estimation, the recovered image is as follows:
in the formula, t0Is the lower limit of the transmittance; a is global atmosphere light; i (x) represents a foggy image;
the method comprises the steps of calculating an atmospheric light value in an L color channel, selecting the first 0.1% pixel points with the largest brightness value from dark primary colors obtained through calculation of a L color channel, taking the maximum value of the pixel points corresponding to an original image as a value of A, substituting the value of A into a transmissivity calculation formula t (x), and finally obtaining a new L color channel through recovery calculation.
And (4) combining L color channel, A channel and B channel values to form a new defogged image, and further converting the image into an RGB image for output and display.
Therefore, through the algorithm processing steps, the haze with uneven distribution is further refined by using a dark channel rule, the defect of high computation complexity of dark primary color prior is overcome, and compared with a classic soft-matting dark color prior defogging algorithm, the algorithm provided by the invention reduces about 1/3 in operation time, so that the defogging of the aerial image under the condition of uneven low-altitude haze is not only quick, but also is more thorough, and local information such as the edge and details of the aerial image is well kept.
Example 2:
referring to fig. 5, the image defogging system provided by the embodiment includes:
a color conversion module 701, configured to convert the foggy image from an RGB color space to an L AB color space, to obtain a foggy image in a L AB color space;
the wavelet transformation denoising processing module 702 is configured to perform wavelet transformation denoising processing on L and A, B color channels of the hazy image in the L AB color space, respectively, to obtain denoised L and A, B color channels;
and the dark primary color prior processing module 703 is configured to perform dark primary color prior processing on the L color channel image according to the transmittance distribution to obtain a new L color channel.
A merging module 704, configured to merge the processed L and A, B color channels to obtain a L AB image, and further convert the image into an RGB image;
therefore, the system is based on the requirements of atomization and the requirement of the needed clear processing of aerial images in foggy days, and the advantages of more concentrated information are converted into the basis by utilizing wavelet transformation, so that the defect of high complexity of dark primary color prior calculation is overcome, and the aerial images under the condition of uneven low-altitude haze are defogged quickly and more thoroughly.
Since the color conversion module 701, the wavelet transformation denoising processing module 702, the dark primary color prior processing module 703 and the combining module 704 respectively correspond to step 101-104 of embodiment 1, the working principle of each module is not described again in this embodiment.
Example 3:
referring to fig. 6, the image defogging processing terminal provided by the embodiment includes a processor 801, a memory 802, and a computer program 803, such as an image defogging processing program, stored in the memory 802 and operable on the processor 801. The processor 801, when executing the computer program 803, implements the steps of embodiment 1 described above, such as the steps shown in fig. 1. Alternatively, the processor 801 implements the functions of the modules in embodiment 2 when executing the computer program 803.
Illustratively, the computer program 803 may be partitioned into one or more modules that are stored in the memory 802 and executed by the processor 801 to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 803 in the image defogging processing terminal.
The image defogging processing terminal can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The image defogging processing terminal can include, but is not limited to, a processor 801 and a memory 802. It will be understood by those skilled in the art that fig. 6 is only an example of an image defogging processing terminal, and does not constitute a limitation of the image defogging processing terminal, and may include more or less components than those shown, or some components may be combined, or different components may be included, for example, the image defogging processing terminal may further include an input/output device, a network access device, a bus, and the like.
The Processor 801 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable Gate Array (FPGA) or other programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 802 may be an internal storage element of the image defogging processing terminal, such as a hard disk or a memory of the image defogging processing terminal. The memory 802 may also be an external storage device of the image defogging processing terminal, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the image defogging processing terminal. Further, the memory 802 may also include both an internal storage unit and an external storage device of the image defogging processing terminal. The memory 802 is used for storing the computer program and other programs and data required by the image defogging processing terminal. The memory 802 may also be used to temporarily store data that has been output or is to be output.
Example 4:
the present embodiment provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the method of embodiment 1.
The computer-readable medium can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
The above embodiments are only for illustrating the technical concept and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention accordingly, and not to limit the protection scope of the present invention accordingly. All equivalent changes or modifications made in accordance with the spirit of the present disclosure are intended to be covered by the scope of the present disclosure.
Claims (10)
1. An image defogging method, comprising:
converting the foggy image from the RGB color space to L AB color space to obtain a foggy image of L AB color space;
respectively carrying out wavelet transformation denoising processing on L and A, B color channels of the foggy image of L AB color space to obtain denoised L and A, B color channels;
dark channel prior processing is performed on the luminance component L color channel;
merging the processed L and A, B color channels to obtain a L AB image;
the L AB image is converted into an RGB image, and the defogged image is output.
2. The image defogging method according to claim 1, wherein a threshold method is adopted to perform wavelet transformation denoising processing on the subcomponents of the L and A, B three color channels.
3. The image defogging method according to claim 1, wherein said dark primary color prior processing of the luminance component L color channel comprises:
according to the dark channel prior theory, for a fog-free image j (x), the dark channel prior law is:
in the formula, Jdark(x) Color values of the dark channel map of J (x); j. the design is a squarec(y) is the color value of one of the r, g, b channels in J (x); Ω (x) is a region centered at x; c represents any one color channel of r, g and b;
the transmission t (x) is calculated as:
after a dark channel check algorithm and transmittance estimation, the recovered image is as follows:
in the formula, t0Is the lower limit of the transmittance; a is global atmosphere light; i (x) represents a foggy image;
the method comprises the steps of calculating an atmospheric light value in an L color channel, selecting the first 0.1% pixel points with the largest brightness value from dark primary colors obtained through calculation of a L color channel, taking the maximum value of the pixel points corresponding to an original image as a value of A, substituting the value of A into a transmissivity calculation formula t (x), and finally obtaining a new L color channel through recovery calculation.
4. The image defogging method according to claim 2, wherein said wavelet transformation denoising of the subcomponents of the L and A, B color channels by means of the threshold method comprises:
wavelet decomposition is carried out on the signal containing the noise on each scale, and all decomposition values under the large scale are reserved; setting a threshold value for the decomposition value under the small scale, setting the wavelet coefficient with the amplitude lower than the threshold value to zero, completely retaining the wavelet coefficient higher than the threshold value, and finally reconstructing the wavelet coefficient obtained after processing by utilizing inverse wavelet transform to recover the effective signal.
5. An image defogging system, comprising:
the color conversion module is used for converting the foggy image from the RGB color space to L AB color space to obtain a foggy image of L AB color space;
the wavelet transformation denoising processing module is used for respectively carrying out wavelet transformation denoising processing on L and A, B color channels of the foggy image of the L AB color space to obtain denoised L and A, B color channels;
the dark primary color prior processing module is used for carrying out dark primary color prior method processing on the L color channel image according to the transmittance distribution;
and the merging module is used for merging the processed L and A, B color channels to obtain a L AB image.
6. The image defogging system according to claim 5, wherein said wavelet transformation denoising processing module adopts a threshold method to perform wavelet transformation denoising processing on the subcomponents of the L and A, B three color channels.
7. The image defogging system according to claim 5, wherein said dark primary prior processing module dark primary prior processing the luminance component L color channel comprises:
according to the dark channel prior theory, for a fog-free image j (x), the dark channel prior law is:
in the formula, Jdark(x) Color values of the dark channel map of J (x); j. the design is a squarec(y) is the color value of one of the r, g, b channels in J (x); Ω (x) is a region centered at x; c represents any one color channel of r, g and b;
the transmission t (x) is calculated as:
after a dark channel check algorithm and transmittance estimation, the recovered image is as follows:
in the formula, t0Is the lower limit of the transmittance; a is global atmosphere light; i (x) represents a foggy image;
calculating an atmospheric light value in an L color channel, selecting the first 0.1% pixel points with the maximum brightness value from the dark primary colors calculated by the L color channel, taking the maximum value of the pixel points corresponding to the original image as the value of A, substituting the value into a transmissivity calculation formula t (x), and finally obtaining a new L color channel through recovery calculation.
8. The image defogging method according to claim 6, wherein the wavelet transformation denoising of the subcomponents of the L and A, B color channels by means of the threshold method comprises:
wavelet decomposition is carried out on the signal containing the noise on each scale, and all decomposition values under the large scale are reserved; setting a threshold value for the decomposition value under the small scale, setting the wavelet coefficient with the amplitude lower than the threshold value to zero, completely retaining the wavelet coefficient higher than the threshold value, and finally reconstructing the wavelet coefficient obtained after processing by utilizing inverse wavelet transform to recover the effective signal.
9. An image defogging processing terminal comprising a memory, a processor and a computer program stored in said memory and executable on said processor, wherein said processor implements the steps of the method according to any one of claims 1 to 4 when executing said computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 4.
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