CN114429426B - Low-illumination image quality improvement method based on Retinex model - Google Patents
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Abstract
A low-illumination image quality improvement method based on a Retinex model belongs to the technical field of image processing. The invention solves the problem of poor quality of the obtained image when the existing low-illumination image quality improvement algorithm is adopted to process the low-illumination image. Firstly, layering a digital image through a Retinex model to obtain a detail layer image and an illumination layer image; secondly, designing a nonlinear global brightness mapping function, and mapping the illumination layer image to obtain an illumination layer enhanced image; designing a nonlinear detail layer image mapping function again, and stretching the detail layer image to obtain a detail layer enhanced image; and finally, multiplying each pixel of the detail layer enhanced image and the illumination layer enhanced image to synthesize the low-illumination enhanced image. The method can be applied to improve the quality of the low-illumination image.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a low-illumination image quality improving method based on a Retinex model.
Background
Because light is easily influenced by the environment in a propagation path, the images shot by the digital camera have uneven brightness distribution, unclear details and textures in low-illumination areas, and poor image quality and human eye visualization effect. Therefore, how to improve the effect of the low illumination effect on the image quality has become a hot issue in the image processing field in recent years.
The classic low-illumination image quality improvement algorithm mainly comprises an image gray scale segmentation mapping algorithm, a histogram equalization algorithm, a Gamma correction algorithm and the like. Although the classic low-illumination image quality improvement algorithm can inhibit the visual problem caused by the low-illumination effect to a certain extent, the image quality of the enhanced image is affected by the problems of selection of free parameters, excessive enhancement of the image, fuzzy details and the like, and therefore, after the low-illumination image is processed by the existing low-illumination image quality improvement algorithm, the quality of the obtained image is still poor.
Disclosure of Invention
The invention aims to solve the problem that when the existing low-illumination image quality improvement algorithm is adopted to process a low-illumination image, the quality of the obtained image is poor, and provides a low-illumination image quality improvement method based on a Retinex model.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a low-illumination image quality improvement method based on a Retinex model specifically comprises the following steps:
step one, after the acquired image is converted from an RGB channel to an HSV channel, data of a V channel, an H channel and an S channel are respectively acquired;
decomposing the V channel data into an illumination layer image and a detail layer image;
step two, carrying out global brightness mapping processing on the illumination layer image to obtain the illumination layer image after global brightness mapping;
then, carrying out pixel value domain expansion on the illumination layer image after global brightness mapping to obtain an illumination layer image after pixel value domain expansion;
stretching the detail layer image by using a detail layer image mapping function to obtain a stretched detail layer image;
step four, synthesizing the illumination layer image with the expanded pixel value domain and the stretched detail layer image into a new image; and converting the synthesized image and the H channel and S channel data obtained in the first step into an output image.
Further, in the first step, the V-channel data is decomposed into an illumination layer image and a detail layer image, and the specific process is as follows:
I(x,y)＝c(x,y)×L(x,y) (1)
wherein, I (x, y) represents a pixel value of the V channel data at the pixel point (x, y), L (x, y) represents a pixel value of the illumination layer image at the pixel point (x, y), and c (x, y) represents a pixel value of the detail layer image at the pixel point (x, y).
Further, the illumination layer image is obtained by performing convolution calculation on the V channel data and a Gaussian kernel with the variance of 1.
Further, the specific process of the second step is as follows:
step two, normalization processing is carried out on the illumination layer image to obtain the illumination layer image after normalization processing;
wherein the content of the first and second substances,
representing the pixel value of the illumination layer image after normalization processing at the pixel point (x, y), wherein max represents the maximum value operation;
secondly, determining a brightness segmentation threshold T of the illumination layer image after normalization processing by adopting a maximum inter-class variance method;
step two, according to the brightness segmentation threshold value T and a global brightness mapping function, global brightness mapping is carried out on the illumination layer image after normalization processing, and the illumination layer image after global brightness mapping is obtained;
the global luminance mapping function is:
wherein the content of the first and second substances,
representing the pixel value of the illumination layer image after global brightness mapping at the pixel point (x, y);
step two, carrying out pixel value domain expansion on the illumination layer image after global brightness mapping:
wherein L is d And (x, y) represents the pixel value of the illumination layer image after the pixel value domain expansion at the pixel point (x, y).
Further, the detail layer image mapping function is:
wherein e is the base of the natural logarithm, A, B, D are coefficients of the detail layer image mapping function, and S (c (x, y)) represents the value of the pixel point (x, y) in the stretched detail layer image.
Further, the coefficients a, B, D of the detail layer image mapping function are:
wherein, [ h ] 0 ,h 1 ]For the domain of pixel values c (x, y) in the detail layer image, c (x, y) is E [ h ] 0 ,h 1 ]I.e. h 0 Is the minimum value of the pixel values c (x, y), h, in the detail layer image 1 Is the maximum value of the pixel value c (x, y) in the detail layer image.
Further, in the fourth step, the illumination layer image after the pixel value domain expansion and the stretched detail layer image are synthesized into a new image, and the specific process is as follows:
and multiplying the pixel values at the same position in the illumination layer image after the pixel value domain expansion and the stretched detail layer image, and taking the multiplication result as the pixel value at the corresponding position in the synthesized new image.
The invention has the beneficial effects that:
the invention provides a low-illumination image quality improving method based on a Retinex model, aiming at improving the overall contrast of a low-illumination image and enhancing the detail and texture characteristics of the low-illumination image. Firstly, layering a digital image through a Retinex model to obtain a detail layer image and an illumination layer image; secondly, designing a nonlinear global brightness mapping function, and mapping the illumination layer image to obtain an illumination layer enhanced image; designing a nonlinear detail layer image mapping function again, and stretching the detail layer image to obtain a detail layer enhanced image; and finally, multiplying each pixel of the detail layer enhanced image and the illumination layer enhanced image to synthesize the low-illumination enhanced image. Experimental results show that the algorithm can effectively improve the image quality of the low-illumination image.
Drawings
FIG. 1 is a flowchart of a method for improving the quality of low-illumination images based on Retinex model according to the present invention;
fig. 2 is a graph of a global luminance mapping function of a corresponding illumination layer when a luminance partition threshold T is 0.2;
fig. 3 is a graph of a global luminance mapping function of a corresponding illumination layer when a luminance partition threshold T is 0.5;
FIG. 4 is a graph of a detail layer image mapping function;
wherein, the intensity value range of the detail layer of the original image is [0.1,3 ];
FIG. 5(a) is a first original image;
FIG. 5(b) is an enhanced image corresponding to FIG. 5 (a);
FIG. 6(a) is the original image two;
fig. 6(b) is an enhanced image corresponding to fig. 6 (a).
Detailed Description
First embodiment this embodiment will be described with reference to fig. 1. The method for improving the quality of a low-illumination image based on a Retinex model in the embodiment specifically includes the following steps:
step one, after the acquired image is converted from an RGB channel to an HSV channel, data of a V channel, an H channel and an S channel are respectively acquired;
decomposing the V channel data into an illumination layer image and a detail layer image;
step two, carrying out global brightness mapping processing on the illumination layer image to obtain the illumination layer image after global brightness mapping;
then, carrying out pixel value domain expansion on the illumination layer image after global brightness mapping to obtain an illumination layer image after pixel value domain expansion, namely an illumination enhancement image;
stretching the detail layer image by using a detail layer image mapping function to obtain a stretched detail layer image, namely a detail enhanced image;
step four, synthesizing the illumination layer image with the expanded pixel value domain and the stretched detail layer image into a new image; and converting the synthesized image and the H channel and S channel data obtained in the step one into an output image.
In the embodiment, firstly, an image is input to the RGB-to-HSV module for color conversion; independently carrying out Retinex layering model shown in the formula (1) on the V channel data to obtain an illumination layer image and a detail layer image; carrying out global brightness mapping shown in the formula (3) on the illumination layer image independently to obtain an illumination layer enhanced image; independently mapping the detail layer image shown in the formula (5) to obtain a detail layer enhanced image; multiplying the detail layer enhanced image and the illumination layer enhanced image one by one to synthesize a new image; by the HSV-RGB conversion module, the synthesized new image, original H channel and S channel data are converted into an output image which can be directly displayed, and the quality of the obtained output image is obviously improved.
The second embodiment is as follows: the difference between this embodiment and the first embodiment is that, in the step one, the V-channel data is decomposed into an illumination layer image and a detail layer image, and the specific process is as follows:
I(x,y)＝c(x,y)×L(x,y) (1)
wherein, I (x, y) represents a pixel value of the V channel data at the pixel point (x, y), L (x, y) represents a pixel value of the illumination layer image at the pixel point (x, y), and c (x, y) represents a pixel value of the detail layer image at the pixel point (x, y).
According to the Retinex model, a digital image can be decomposed into an illumination layer image and a detail layer image, the illumination layer image determines the whole brightness and darkness contrast of the image, and the detail layer image determines the details and texture of the image. The pixel point (x, y) is a coordinate in an image coordinate system, and the width direction of the image is taken as an x-axis, and the height direction of the image is taken as a y-axis.
Other steps and parameters are the same as those in the first embodiment.
The third concrete implementation mode: the difference between this embodiment and the first or second embodiment is that the illumination layer image is obtained by performing convolution calculation on V-channel data and a gaussian kernel with a variance of 1.
Other steps and parameters are the same as those in the first or second embodiment.
The fourth concrete implementation mode: the difference between this embodiment and one of the first to third embodiments is that the specific process of the second step is:
step two, normalization processing is carried out on the illumination layer image to obtain the illumination layer image after normalization processing;
wherein the content of the first and second substances,
representing the pixel value of the illumination layer image after normalization processing at the pixel point (x, y), wherein max represents the maximum value operation;
secondly, determining the brightness segmentation threshold T of the illumination layer image after normalization processing by adopting a maximum inter-class variance method (OTSU);
step two, according to the brightness segmentation threshold value T and a global brightness mapping function, global brightness mapping is carried out on the illumination layer image after normalization processing, and the illumination layer image after global brightness mapping is obtained;
the global luminance mapping function is:
wherein the content of the first and second substances,
representing the pixel value of the illumination layer image after global brightness mapping at the pixel point (x, y);
the overall contrast of the image can be enhanced through the global brightness mapping, and as can be seen from fig. 2 and 3, the global brightness mapping function stretches the brightness smaller than the threshold T, so as to improve the brightness of the portion of pixels, and compresses the brightness larger than the threshold T, so as to reduce the brightness of the portion of pixels.
Step two, pixel (brightness) value domain expansion is carried out on the illumination layer image after the global brightness mapping:
wherein L is d And (x, y) represents the pixel value of the illumination layer image after the pixel value domain expansion at the pixel point (x, y).
By stretching the illumination layer image L by the method of the present embodiment, the overall contrast of the low-illumination image can be enhanced.
Other steps and parameters are the same as those in one of the first to third embodiments.
The fifth concrete implementation mode is as follows: this embodiment will be described with reference to fig. 4. The difference between this embodiment and one of the first to the fourth embodiments is that the detail layer image mapping function is:
wherein e is the base of the natural logarithm, A, B, D are coefficients of the detail layer image mapping function, and S (c (x, y)) represents the value of the pixel point (x, y) in the stretched detail layer image.
The nonlinear detail layer image mapping function designed in the present embodiment is to enhance the detail and texture characteristics of the detail layer image and enrich the detail and texture characteristics of the low-illumination image.
Other steps and parameters are the same as in one of the first to fourth embodiments.
The sixth specific implementation mode: this embodiment is different from one of the first to fifth embodiments in that the coefficients a, B, and D of the detail layer image mapping function are:
wherein, [ h ] 0 ,h 1 ]For the domain of pixel values c (x, y) in the detail layer image, c (x, y) is E [ h ] 0 ,h 1 ]I.e. h 0 Is the minimum value of the pixel values c (x, y), h, in the detail layer image 1 Is the maximum value of the pixel value c (x, y) in the detail layer image.
A, B and D are three undetermined coefficients, and the definition domain, the value domain and the original image detail intensity (namely the pixel value) of the decision function are enhanced image detail intensities corresponding to 1. Since the image is most blurred when the original detail layer image intensity is 1, the detail-enhanced image intensity should also be 1, i.e., S (1) ═ 1. In addition, detail layer image enhancement needs to keep the domain consistent with the value domain, i.e., S (h) 0 )＝h 0 、S(h 1 )＝h 1 . Therefore, the three corresponding relations are respectively substituted into the function (5), and the values of the three pending coefficients shown in the formula (6) are obtained.
Other steps and parameters are the same as those in one of the first to fifth embodiments.
The seventh embodiment: the difference between this embodiment and one of the first to sixth embodiments is that, in the fourth step, the illumination layer image after the pixel value domain expansion and the detail layer image after the stretching are synthesized into a new image, and the specific process is as follows:
and multiplying the pixel values at the same position in the illumination layer image after the pixel value domain expansion and the stretched detail layer image, and taking the multiplication result as the pixel value at the corresponding position in the synthesized new image.
That is, the pixel value of the pixel point (x, y) in the illumination layer image after the pixel value domain expansion is multiplied by the pixel value of the pixel point (x, y) in the detail layer image after stretching, and the multiplication result is used as the pixel value of the pixel point (x, y) in the synthesized new image.
Other steps and parameters are the same as those in one of the first to sixth embodiments.
Analysis of Experimental results
The simulation software adopted by the invention is Matlab 2018 a. The hardware platform is a desktop computer, and the hardware of the hardware platform comprises the following components: i9-11900H specification CPU, 16GB DDR4 specification memory, RTX 3060 specification display card. The input and output of the simulation program are standard images in the bmp format.
The simulation results of the method of the present invention are shown in fig. 5(a) and 5(b) and in fig. 6(a) and 6 (b).
As can be seen from fig. 5(a) and 5(b) and fig. 6(a) and 6(b), because the illumination intensity is relatively low and the distribution is not uniform, the original image frame shows a darker effect, so that the details and the contour of the scene in the original image cannot be distinguished by human eyes, and thus the image quality is poor; the low-illumination image quality improvement algorithm designed by the invention improves the image brightness of a low-illumination area, and the local details of the enhanced image are clear and rich. Therefore, the algorithm can effectively improve the overall visualization effect of the low-illumination image and enhance the image quality of the image.
The above-described calculation examples of the present invention are merely to explain the calculation model and the calculation flow of the present invention in detail, and are not intended to limit the embodiments of the present invention. It will be apparent to those skilled in the art that other variations and modifications of the present invention can be made based on the above description, and it is not intended to be exhaustive or to limit the invention to the precise form disclosed, and all such modifications and variations are possible and contemplated as falling within the scope of the invention.
Claims (5)
1. A low-illumination image quality improvement method based on a Retinex model is characterized by specifically comprising the following steps of:
step one, after the acquired image is converted from an RGB channel to an HSV channel, data of a V channel, an H channel and an S channel are respectively acquired;
in the first step, the V-channel data is decomposed into an illumination layer image and a detail layer image, and the specific process is as follows:
I(x,y)＝c(x,y)×L(x,y) (1)
wherein, I (x, y) represents a pixel value of the V channel data at the pixel point (x, y), L (x, y) represents a pixel value of the illumination layer image at the pixel point (x, y), and c (x, y) represents a pixel value of the detail layer image at the pixel point (x, y);
decomposing the V channel data into an illumination layer image and a detail layer image;
step two, carrying out global brightness mapping processing on the illumination layer image to obtain an illumination layer image after global brightness mapping;
the specific process of the second step is as follows:
step two, normalizing the illumination layer image to obtain a normalized illumination layer image;
wherein the content of the first and second substances,
representing the pixel value of the illumination layer image after normalization processing at the pixel point (x, y), wherein max represents the maximum value operation;
secondly, determining a brightness segmentation threshold T of the illumination layer image after normalization processing by adopting a maximum inter-class variance method;
step two, according to the brightness segmentation threshold value T and a global brightness mapping function, global brightness mapping is carried out on the illumination layer image after normalization processing, and the illumination layer image after global brightness mapping is obtained;
the global luminance mapping function is:
wherein the content of the first and second substances,
representing a global luminance mapThe pixel value of the subsequent illumination layer image at the pixel point (x, y);
step two, carrying out pixel value domain expansion on the illumination layer image after global brightness mapping:
wherein L is d (x, y) represents the pixel value of the illumination layer image at the pixel point (x, y) after the pixel value domain expansion;
then, carrying out pixel value domain expansion on the illumination layer image after global brightness mapping to obtain an illumination layer image after pixel value domain expansion;
stretching the detail layer image by using a detail layer image mapping function to obtain a stretched detail layer image;
step four, synthesizing the illumination layer image with the expanded pixel value domain and the stretched detail layer image into a new image; and converting the synthesized image and the H channel and S channel data obtained in the first step into an output image.
2. The method as claimed in claim 1, wherein the illumination layer image is obtained by performing convolution calculation on V-channel data and a gaussian kernel with a variance of 1.
3. A method of improving image quality under low illumination based on Retinex model as claimed in claim 2, wherein the detail layer image mapping function is:
wherein e is the base of the natural logarithm, A, B, D are coefficients of the detail layer image mapping function, and S (c (x, y)) represents the value of the pixel point (x, y) in the stretched detail layer image.
4. The method of claim 3, wherein coefficients A, B, and D of the detail layer image mapping function are:
wherein, [ h ] 0 ,h 1 ]For the domain of pixel values c (x, y) in the detail layer image, c (x, y) is E [ h ] 0 ,h 1 ]I.e. h 0 Is the minimum value of the pixel values c (x, y), h, in the detail layer image 1 Is the maximum value of the pixel value c (x, y) in the detail layer image.
5. The method according to claim 4, wherein in the fourth step, the illumination layer image after pixel value domain expansion and the detail layer image after stretching are synthesized into a new image by a specific process:
and multiplying the pixel values at the same position in the illumination layer image after the pixel value domain expansion and the stretched detail layer image, and taking the multiplication result as the pixel value at the corresponding position in the synthesized new image.
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