Low-illumination image enhancement method based on mayfly naiad algorithm and adaptive tone mapping

Abstract

The problems of low brightness, fuzzy details and the like in a low-illumination image are solved. The invention discloses a low-illumination image enhancement method based on a mayfly naiad algorithm and adaptive tone mapping. The method comprises the following steps: firstly, converting an RGB picture into an HSV space to reduce the relevance among brightness information, chrominance information and saturation information; and then the brightness information is segmented by using a mayfly naiad algorithm to obtain different area sub-images, and the brightness information is enhanced by using an improved block adaptive tone mapping method. In order to prevent chromatic aberration, a white balance correction method is adopted to restore color permeability. Meanwhile, detail textures are enhanced through a parameter physicochemical anti-sharpening mask algorithm; and finally, performing weighted fusion on the enhanced brightness information and the detail information by using a multi-scale fusion algorithm, and converting back to an RGB space to obtain an RGB image with proper brightness and rich details. Experiments prove that the method effectively improves the image brightness and contrast, and the visual effect is remarkably improved.

Description

Technical Field

[0001] The invention belongs to the field of image enhancement, and in particular relates to a low-illumination image enhancement method based on an ephemeral algorithm and adaptive tone mapping. Background Art

[0002] Images are one of the main sources of information for people and are closely related to people's daily lives. However, image data collected under poor lighting conditions usually show problems such as insufficient local contrast, low overall brightness, blurred detail areas, and weak visual information expression. This makes it difficult to clearly present key information when observing and analyzing images, affecting the intuitive effect and practicality of the image. In order to solve this problem, low-light image enhancement technology uses a series of targeted processing steps, such as improving local contrast, adjusting overall brightness, color enhancement, sharpening details, and enhancing the clarity of visual information, in order to achieve the purpose of improving image quality. With the continuous development of image enhancement technology, not only has the visual presentation effect of images been improved, but the operating performance of computer vision systems in variable lighting environments has also made great progress. Therefore, the research and application of low-light image enhancement technology plays a vital role in meeting the needs of computer vision systems for high-quality images. Summary of the invention

[0003] The purpose of the present invention is to enhance low-illuminance images, requiring the enhanced images to have appropriate brightness, rich details and high contrast.

[0004] In order to achieve the above object, the present invention adopts the following technical solution: a low illumination image enhancement method based on the ephemera algorithm and adaptive tone mapping, comprising the following steps:

[0005] Step 1: Convert the RGB image to HSV space and extract the brightness information ImageV, chroma information ImageH and saturation information ImageS respectively.

[0006] Step 2: Use the ephemera algorithm to segment the extracted image brightness information ImageV to obtain multiple sub-images with different brightness in different regions.

[0007] Step 3: Use the improved block adaptive tone mapping method to enhance the brightness information of the separated image according to the average brightness of each sub-image obtained by solving the problem, and then obtain ImageV i .

[0008] Step 4: Use a parametric rationalized unsharp masking algorithm to process the extracted image brightness information ImageV, enhance the image detail texture information, and obtain ImagePRUM.

[0009] Step 5: Take the multiple enhanced sub-images ImageV obtained in step 3 i , Gaussian and Laplace pyramid methods are used for multi-scale weighted fusion to obtain ImageV c .

[0010] Step 6: ImageV c ImagePRUM is combined with the Gaussian and Laplace pyramid methods to obtain ImageV e Finally, the image ImageV e The chromaticity information ImageH and saturation information ImageS of the original image are converted to RGB space to obtain an image with appropriate brightness and rich details.

[0011] The effect brought by the present invention is that the proposed low-light image enhancement method can effectively improve image brightness, increase image contrast, and effectively restore image detail information. By converting the RGB image into the HSV space, the correlation between brightness information, chromaticity information, and saturation information is reduced, thereby avoiding the influence of changing brightness information on color information and effectively preventing the occurrence of image color distortion. The separated brightness information is processed so that the overall brightness of the low-light image is enhanced while retaining detail information, and the visual effect is significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is the algorithm principle diagram in the embodiment of the present invention

[0013] Figure 2 This is a block adaptive tone mapping principle diagram in an embodiment of the present invention.

[0014] Figure 3 : is a block diagram of a parameter rationalized unsharp masking algorithm in an embodiment of the present invention

[0015] Figure 4 is a block diagram of a multi-scale weighted fusion algorithm in an embodiment of the present invention

[0016] Figure 5 This is a block diagram of the algorithm principle in the embodiment of the present invention.

[0017] Figure 6 This is a low-light image enhancement effect diagram in an embodiment of the present invention.

[0018] like Figure 1As shown, the present invention provides a low-illumination image enhancement method, which mainly divides the brightness information into sub-images of different regions through the ephemera algorithm, and adopts an adaptive tone mapping method to enhance the brightness information, and at the same time enhances the detail texture through a parameter rationalized unsharp masking algorithm, and then weightedly fuses the enhanced brightness information with the detail information through a multi-scale weighted fusion method, and converts it back to the RGB space, and finally obtains a brightness-enhanced picture. DETAILED DESCRIPTION

[0019] Specifically, in step one:

[0020] Since the scene brightness information in the RGB color space is highly correlated with the color saturation, the color information will be affected when the brightness information is enhanced. However, the correlation between the color information and the brightness information in the HSV color space is very small, which avoids the color distortion problem caused by the enhancement of the brightness information. Therefore, the image is converted to the HSV space to prepare for the subsequent enhancement processing. The specific formulas for converting the RGB space to the HSV space are shown in (1), (2), and (3).

[0021] V=max(R,G,B) (1)

[0022]

[0023] Specifically, in step 2:

[0024] In order to obtain sub-images with different brightness, the brightness values ​​of the image ImageV are first classified in the image region block, and each gray level is [0,1,2,...,L-1], and L=256 in an 8-bit image. If the total number of pixels is MN, and the number of pixels with gray level i is n, then the probability of gray level i appearing in the image is approximately p i =n / MN, and then traverse all gray levels i in the image in turn to obtain the probability of each gray level appearing, and the corresponding probability is [p0,p1,p2,...,p L-1 Then, the ephemera algorithm is used to search for the optimal separation threshold corresponding to the probability. In the search, the maximum exponential entropy threshold is used as the appropriate function to search for the optimal separation threshold T0, T1, T2, ..., T in the brightness information of imageV. L-1 , the image can be divided into L categories at most, where the threshold segmentation expression is given by formula (4).

[0025]

[0026] Where H0 is the target class entropy value corresponding to the separation threshold T, H k is the background class entropy value corresponding to the separation threshold T, and the expression is given by equations (5) and (6).

[0027]

[0028] where h i is the entropy after the i-th segmentation, p i is the probability of the pixel with gray value i appearing, ω0 is the probability of the initial category appearing, ω k is the probability of the kth category appearing.

[0029] The multi-threshold value is expressed as T1 = (μ0 + μ1) / 2, T2 = (μ1 + μ2) / 2, ..., T L-1 =(μ L-2 +μ L-1 ) / 2, each category is represented by R0={0,1,2,...,T0},...,R k ={T k +1,...,T k+1},...,R L-1 ={T L-1 +1,...,L-1}, the mean of each category is denoted by μ0,μ1,μ2,...,μ L-1 , the probability of each type appearing is recorded as [P0,P1,P2,...,P L-1 ], the specific formula is expressed as

[0030]

[0031] Where k = 0, 1, 2, ..., L-1, and the average grayscale value of the image is expressed as

[0032]

[0033] Specifically, in step three:

[0034] First, the low-light image is threshold-segmented according to the Mayfly algorithm to obtain sub-images of different brightness areas, and then the brightness mean of the sub-images is calculated according to formula (11).

[0035]

[0036] Where M i is the number of pixels of a specific sub-image. According to this formula, the average brightness of different divided areas can be calculated.

[0037] Then, the image is adaptively stretched in brightness according to the following mapping method, that is, the improved block adaptive tone mapping method mentioned in the present invention. The following formula is designed

[0038]

[0039]

[0040] In formula (12), is the output of global tone mapping, L dmax is the brightness level of the displayed medium, and L is selected in this paper. dmax =256, L w (x, y) is the brightness information of the image V channel, L wmax and L wmin are the maximum and minimum brightness values ​​in the image, respectively. and The brightness mean, maximum brightness and minimum brightness of each sub-image after the original image is segmented are obtained. i .

[0041] Finally, in order to remove the color temperature deviation, maintain the scene color, and ensure that the white in the image appears pure white after the brightness is stretched, the white balance method is used to correct and optimize the color temperature deviation and improve the color expression of the image. The white balance expression is

[0042]

[0043] In formula (14), f(x) is the white balance correction output, x is the specific image pixel value that needs to be corrected, V max With V min are the maximum and minimum pixel values ​​of the image to be corrected, respectively. max and min are the specified pure white and pure black pixel values, respectively. Here max = 255, min = 0.

[0044] Specifically, in step 4:

[0045] The process of the parameterized rationalized unsharp masking algorithm is to transform the original image L w After (x, y) is converted to the frequency domain, nonlinear filtering is performed to extract the high-frequency component Z(x, y) in the original image. The definition of the nonlinear filter is shown in formula (15).

[0046] Z(x,y)=μ x F x (x,y)C x (x,y)+μ y F y (x,y)C y (x,y) (15)

[0047] In formula (15), μ x and μ y are the gradient adjustment factors in the horizontal and vertical directions respectively, and the other calculation items are given by formula (16).

[0048]

[0049] In formula (16), L w (x, y) is the pixel value at the (x, y) position in the original image, and δ is a very small parameter to prevent singularity in the gradient operator in the horizontal and vertical directions.

[0050] Then the high-frequency component Z(x,y) is subjected to a specific mapping process to obtain Z c (x,y). Then the processed high-frequency component Z c (x,y) and the original image L w (x, y) are fused, and finally a brightness image ImagePRUM that highlights texture details is obtained. The fusion process is shown in formula (17).

[0051]

[0052] In formula (17), L e (x, y) is the output image after filtering by the parameterized rationalized unsharp masking algorithm, Z c (x, y) is the output result after algorithm mapping, L wmax is the maximum value of pixels in the original image Lw(x,y).

[0053] Specifically, in step five:

[0054] The multiple enhanced sub-images ImageV obtained in step 3 i , Gaussian and Laplace pyramid methods are used for multi-scale weighted fusion to obtain ImageV c The multi-scale fusion process is shown in equations (18), (19), and (20).

[0055]

[0056] In formula (18), l represents the number of pyramid layers, I l (x, y) is the output result of the Gaussian and Laplacian pyramids that fuse the information of each sub-image, that is, the multi-scale fusion result, D k (x,y) represents the kth subgraph, Represents the normalized weight map of the Gaussian pyramid. The standard deviation of the Gaussian filter in the algorithm is σ=0.33.

[0057] Specifically, in step six:

[0058] ImageV c ImagePRUM is combined with the Gaussian and Laplace pyramid methods to obtain ImageV e Finally, the image ImageV eThe chromaticity information ImageH and saturation information ImageS of the original image are converted to RGB space to obtain an image with appropriate brightness and rich details. The HSV to RGB space method is shown in equations (21) and (22):

[0059]

[0060] (R, G, B) = ((R'+m) × 255, (G'+m) × 255, (B'+m) × 255) (22) where P = V × S, m=VC.

[0061] Finally, it should be noted that the above contents are only preferred embodiments of the present invention and are not intended to limit the scope of the present invention. The above embodiments have been described in detail for the present invention, but for technicians working in related fields, it is still possible to make appropriate adjustments to the technical solutions therein, or even to perform equivalent substitutions on certain technical features. As long as these adjustments, equivalent substitutions or further improvements still comply with the basic spirit and principles of the present invention, they should be included in the protection scope of the present invention.

Claims

1. A low-light image enhancement method based on the ephemera algorithm and adaptive tone mapping, characterized in that: include: Step 1: Convert the RGB image to HSV space and extract the brightness information ImageV, chroma information ImageH and saturation information ImageS respectively. Step 2: Use the ephemera algorithm to segment the extracted image brightness information ImageV to obtain multiple sub-images with different brightness in different regions. Step 3: Use the improved block adaptive tone mapping method to enhance the brightness information of the separated image according to the average brightness of each sub-image obtained by solving the problem, and then obtain ImageV i . Step 4: Use a parametric rationalized unsharp masking algorithm to process the extracted image brightness information ImageV, enhance the image detail texture information, and obtain ImagePRUM. Step 5: Take the multiple enhanced sub-images ImageV obtained in step 3 i , Gaussian and Laplace pyramid methods are used for multi-scale weighted fusion to obtain ImageV c . Step 6: ImageV c ImagePRUM is combined with the Gaussian and Laplace pyramid methods to obtain ImageV e Finally, the image ImageV e The chromaticity information ImageH and saturation information ImageS of the original image are converted to RGB space to obtain an image with appropriate brightness and rich details.

2. According to the low-illumination image enhancement method based on the ephemera algorithm and adaptive tone mapping described in claim 1, its main feature is that: in step 1, the RGB image is converted to the HSV space, and the brightness information ImageV, the chromaticity information ImageH and the saturation information ImageS are extracted respectively, so that the correlation between the three is reduced.

3. According to the low-illumination image enhancement method based on the mayfly algorithm and adaptive tone mapping described in claim 1, its main feature is that: in step 2, the mayfly algorithm is used to perform multi-threshold image segmentation on the extracted image brightness information ImageV to obtain sub-images with different brightness in multiple regions.

4. According to the method for low-light image enhancement based on the ephemera algorithm and adaptive tone mapping as described in claim 1, the main feature is that: in step 3, an improved block adaptive tone mapping method is used to enhance the brightness information of the separated image according to the brightness average value of each sub-image obtained by solving, and then Image V is obtained. i .

5. According to the low-illumination image enhancement method based on the ephemera algorithm and adaptive tone mapping described in claim 1, its main feature is that: in step 4, a parameter rationalized unsharp masking algorithm is used to process the extracted image brightness information ImageV to enhance the detail texture information of the image and obtain ImagePRUM.

6. The low-light image enhancement method based on the ephemera algorithm and adaptive tone mapping according to claim 1, wherein: in step 5, the plurality of enhanced sub-images ImageV obtained in step 3 are i , Gaussian and Laplace pyramid methods are used for multi-scale weighted fusion to obtain ImageV c .

7. The low-light image enhancement method based on the ephemera algorithm and adaptive tone mapping according to claim 1, wherein: in step 6, the image ImageV obtained in step 5 is c The image ImagePRUM obtained in step 4 is fused with multi-scale weighted fusion using Gaussian and Laplace pyramid methods to obtain ImageV e Finally, the image ImageV e The chromaticity information ImageH and saturation information ImageS of the original image are converted to RGB space to obtain an image with appropriate brightness and rich details.

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