CN111539936B - Mixed weight multispectral fusion method of lithium battery image - Google Patents
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Abstract
The invention provides a mixed weight multispectral fusion method of a lithium battery image. Firstly, analyzing the difference of multiple defect information spectrums, collecting a group of multispectral images with specific wavelengths, and selecting the optimal five fusion wavebands and the optimal fusion space. And then, according to the difference of the representation forms of the three channels of the optimal fusion space of the multispectral defect image, each channel is independently processed, and the defect characteristics are highlighted and the complex background is restrained. The result shows that the fusion result of the method has smoother background and more prominent multi-type characteristics, enhances the defect part while inhibiting the complex background, accords with the identification of a human visual system on a defect region, has better performance, can reduce the interference of complex textures on the surface of the lithium battery piece on defect detection, and obtains clear multi-type surface defect images.
Description
Technical Field
The invention belongs to the field of image fusion, and particularly relates to a mixed weight multispectral fusion method of a lithium battery image.
Background
Lithium batteries are a type of batteries using a nonaqueous electrolyte solution with lithium metal or a lithium alloy as a negative electrode material. Lithium batteries can be broadly divided into two categories: lithium metal batteries and lithium ion batteries, which are metal-free lithium and rechargeable. At present, lithium ion batteries are widely used in actual daily life of people, and are commonly called lithium batteries.
The surface of the lithium battery is damaged to different degrees due to the influence of reasons such as materials, machine equipment, manual misoperation and the like in factory production, and defects such as scratches, shrinkage cavities and the like are generated. The defect information features are highly similar to the complex background features of the lithium battery, so that the detection difficulty is increased. Quality control of lithium batteries has become a major issue to be addressed.
The lithium battery image has more defects, all defects cannot be clearly presented under a single condition, the subsequent detection difficulty is high, and Anwar et al (Anwar S A, abdullah M Z. Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique [ J ]. Eurasip Journal on Image & Video Processing,2014 (1): 1-17) propose a detection method based on defect gradient characteristics, but the detection method is easy to be interfered by complex backgrounds. Zhao Peng et al (Zhao Peng, wang Nihong, pu Zhaobang. Image fusion based on morphological wavelet decomposition pyramid [ J ]. Photoelectron laser, 2008,19 (6): 814-817.) image fusion was performed by combining wavelet decomposition with image contrast pyramid using the morphological wavelet decomposition pyramid method, but the fused image failed to suppress local noise generation.
Therefore, the difficulty brought by the current method can be effectively improved and solved, and the method is an intensive research work of professionals. Under the condition, a mixed weight multispectral fusion algorithm of the lithium battery image is developed, the influence of a complex background is weakened, noise is suppressed, defect characteristics are enhanced, the contrast ratio of a defect part and a non-defect part is improved, the difficulty of subsequent detection and classification is reduced to the greatest extent, and the lithium battery image mixed weight multispectral fusion algorithm has high practical value.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a mixed weight multispectral fusion method of a lithium battery image. According to the method, a multispectral imaging mode is adopted, the multispectral image with fixed wavelength is obtained by utilizing the spectrum range difference of the defect type, the influence of a complex background is weakened by an image mixing weight fusion method, the defect characteristics are enhanced, and the detection difficulty is reduced to the greatest extent.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a mixed weight multispectral fusion method of lithium battery images comprises the following steps:
the first step: multispectral lithium battery image acquisition:
respectively acquiring images of the same lithium battery in 380nm-505nm wave bands, 530nm-630nm wave bands and 655nm-780nm wave bands to form a multispectral RGB battery source image, and acquiring at least one image of the lithium battery with corresponding wavelength in each wave band;
and a second step of: RGB conversion HSV:
converting the multispectral RGB battery source images with different wave bands selected in the first step from RGB space to HSV space, and then respectively processing H, S, V three-channel images independently;
the conversion relation from RGB to HSV is as follows:
wherein X, Y, Z is chromaticity coordinates, R, G, B is the color representing three channels of red, green and blue;
and a third step of: fusing the S channel images:
3-1) decomposing the S channel image obtained in the second step into a detail layer sub-image and an approximate layer sub-image by using empirical wavelet decomposition;
3-2) generating JND model-based saliency weights
Defining significance weights J (x, y) for the S channel images obtained in the second step by using a nonlinear additive JND model, and extracting pixels with good saturation and contrast in different spectrum images;
wherein and />Is background brightness masking and image visible texture masking, K l,t (0<K l,t < 1) represents an overlap of masking effects; x, y represent chromaticity coordinates; i=1 to N, N being the number of input source image sequences;
wherein I (x, y) is an intensity value, B (a, B) is a low-pass weight filter, and (a, B) represents the coordinate position of the low-pass weight filter B;
wherein ,is a convolution descriptor, g k (x, y) is the kth directional high pass filter, defined as:
3-3) generating saturation weight for the multispectral RGB battery source image obtained in the first step, and calculating saturation weight S according to standard deviation of R, G, B three channels i (x,y):
3-4) combining the saturation weight and the significance weight and normalizing the two to obtain a mixed weight W i (x, y), specifically defined as:
wherein Si (x, y) represents saturation weight, J i (x, y) represents JND saliency weight;
3-5) fusing the detail layer sub-images obtained in the step 3-1) by using a mixed weight according to the following rule to obtain fused detail layer sub-images,
wherein R (x, y) represents the pixel value of the coordinate position of the mixed weight fusion result, D i (x, y) represents a layer of detail layer pixel values of the empirical wavelet decomposition of the ith S-channel image;
3-6) carrying out absolute value large fusion on the approximate layer sub-image obtained in the step 3-1) to obtain a fused approximate layer sub-image;
3-7) fusing the fused approximate layer sub-image and the fused detail layer sub-image to obtain a fused S-channel image;
fourth step: fusion of H-channel images:
the H channel image contains tone information, gaussian filtering is firstly carried out on the H channel image to reduce the influence of noise, and then absolute value large fusion is carried out to obtain a fused H channel image;
fifth step: fusion of V-channel images:
discarding V channel images in 380nm-505nm wave bands and 530nm-630nm wave bands, only fusing V channel images in 655nm-780nm wave bands, firstly performing Gaussian filtering on the V channel images to reduce the influence of noise, and then performing selective absolute value large fusion to obtain fused V channel images;
sixth step: HSV image conversion after fusion:
and finally, fusing the fused V-channel image, the fused H-channel image and the fused S-channel image to obtain a fused HSV image, and converting the HSV image back to an RGB image to obtain a final fused lithium battery image.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, according to the difference of defect information spectrum presentation, the mixed weight multispectral fusion method of the lithium battery image is provided, each channel is independently processed according to the difference of the representation forms of three channels of the multispectral defect image optimal fusion space, the defect characteristics are highlighted, the complex background is restrained, the recognition of the defect area by the human visual system is met, and the method has good performance.
The multispectral mixed weight fusion method of the lithium battery image can meet the requirement of detecting the subsequent defects on the surface of the electrode of the lithium battery in the actual lithium battery industrial production, improve the accurate extraction efficiency of the surface defects, effectively prevent unqualified lithium batteries from flowing into the subsequent links, and further improve the quality and efficiency of quality inspection, and further improve the quality and production efficiency of the lithium battery module.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a color matching function.
Fig. 3 is an RGB chromaticity diagram.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention discloses a mixed weight multispectral fusion method of a lithium battery image, which comprises the following steps:
the first step: multispectral lithium battery image acquisition:
according to the invention, five wavelengths of 380nm, 605nm, 630nm, 655nm and 680nm are selected as the fusion source images of the lithium battery to be fused, the reflection of the 380nm-505nm wave band images is strong, and certain surface information is reserved; the image with the wave band of 530nm-630nm has higher brightness and saturation, the image layering property is better under the two wavelengths of 605nm and 630nm, and the defect characteristics are most obvious; the 655nm-780nm wave band image contains lower energy, weaker brightness and most obvious image defect characteristics at two wavelengths of 655nm and 680 nm.
And a second step of: RGB conversion HSV
Colorimetry is the science of researching visual laws of colors, color measurement theory and technology, and is based on the colorimetry system specified by the same international protocol to perform physical measurement on colors, and the international commission on illumination (CIE) has established a color matching function in 1931:
wherein c (lambda) is a color matching function, and the numbers of the three primary colors of red, green and blue corresponding to the isoelectric spectral color with the wavelength lambda are recorded as r, g and b.
The RGB to wavelength conversion relationship is:
wherein x, y and z are chromaticity coordinates, and R, G, B is the color representing three channels of red, green and blue.
According to the invention, an image quality evaluation index of SSIM (structural similarity) is selected to measure the relation among three channels of each color space, and compared with two spaces of RGB and YCbCr, the HSV space has the smallest similarity, that is, the difference of information contained in each channel is larger.
And a third step of: fusing the S channel images:
3-1) decomposing the S channel image obtained in the second step into a detail layer sub-image and an approximate layer sub-image by using empirical wavelet decomposition;
the empirical wavelet decomposition is a self-adaptive method, which generates a set of filters according to the characteristics of the signal itself, and filters the S-channel image of the multispectral lithium battery by using the generated filters to obtain detailed layer and approximate layer sub-images. The process of empirical wavelet decomposition is:
a constructing a line filter, performing fast Fourier transform on each line of the S channel image to obtainTaking the mean F of the Fourier spectrum of all the rows of the current image row :
wherein ,Nrow The number of lines of the S channel image;
b determining the number of filters according to the designated No value by using an edge detection method, taking the median between two adjacent maximum values of the first No of the Fourier spectrum of the signal, and determining the No-1 edges omega of the divided spectrum n (1.ltoreq.n.ltoreq.No-1) while assuming ω 0 =0,ω No =π;
ω represents frequency and β can be any [0,1] function
wherein ,NR Row represents the row for the number of wavelet functions in the row filter bank, where no=n R 。
F repeating the steps 3-1 to 3-5, processing each column of the image, and constructing a column filter
wherein ,Nc For the number of wavelet functions in the column filter bank, col represents the column, where no=nc.
G filtering the image according to the use of the filter in the line filter bank to obtain N R +1 intermediate results, each of which is then filtered using a column filter to obtain the final (N R +1)*(N c +1) subband images.
For simplicity purposes it can be assumed that τ n =λω n (0 < lambda < 1) so that only one parameter lambda is required, instead of assigning tau to the scale function and each wavelet function n 。
3-2) JND model-based saliency weighting of S-channel image generation
JND refers to the Human Visual System (HVS) minimum perceived difference and defines a perception threshold to guide perceived image quality metric masking. It is an important visual saliency cue for the measurement of image quality. The present invention defines a saliency weight J (x, y) using a nonlinear addable JND model that is capable of extracting pixels with good saturation and contrast in different spectral images.
wherein and />Background luminance masking and image visible texture masking. K (K) l,t (0<K l,t < 1) represents the overlap of masking effects, x, y represents chromaticity coordinates.
The invention uses two different functions to model brightnessAnd average background brightness. One is the root equation for low background luminance (less than 127) and the other is the approximately linear equation for the higher 127 portion, defined as:
where I (x, y) is an intensity value, B (a, B) is a low-pass weight filter, and (a, B) represents the coordinate position of the low-pass weight filter B, and 1.ltoreq.a, b.ltoreq.5.
Texture masking is typically determined by the local spatial gradient around the pixel. To obtain a better alignmentThe edge and non-edge regions should be well distinguished for a deterministic JND estimation. The invention is defined taking into account edge differences, since the HVS draws more attention to the edge structure
wherein ,is a convolution descriptor, g k (x, y) is the kth directional high pass filter, defined as:
3-3) generating saturation weights for multispectral lithium battery raw images
The saturation weight is considered to better preserve the saturation information of the source image sequence, so that the saturation of the fusion result is improved, the saturation weight is generated for the original image of the multispectral lithium battery, and the saturation weight can be calculated according to the standard deviation of R, G, B three channels:
wherein Si For the color saturation of the image,rxy, gxy, bxy represents color saturation on three channels R, G, B, respectively;
3-4): mixing weights
The detail layer capacity is smaller, defect information is contained, and the mixed weight generated by the saturation weight and the saliency weight is used for fusion; in order to obtain a mixed weight of the ith Zhang Yuan image sequence considering the saturation weight and the significance weight, the method combines the two weights and normalizes the two weights to obtain the mixed weight:
wherein Wi (x, y) represents the mixed weight, S i (x, y) represents saturation weight, J i (x, y) represents JND saliency weight.
3-5) fusion rules
Wherein R (x, y) represents the pixel value of the coordinate position of the mixed weight fusion result, D i (x, y) represents a layer of detail layer pixel values of the empirical wavelet decomposition of the ith S-channel image; n is the number of input source image sequences.
3-6) carrying out absolute value large fusion on the sub-images of the approximate layer
The approximate layer capacity occupies a relatively large area, reflects the characteristics of the whole texture structure, and selects the absolute value to take the large fusion strategy, so that the edge strength after fusion is high, and the texture is clear.
wherein ,(mS ,n S ) For the S-channel coordinate position, c S (m S ,n S ) Representing the pixel values after the approximate layer fusion, the pixel values of the five wavelength approximate layer sub-images respectively representing 380nm, 605nm, 630nm, 655nm and 680nm are fused at the corresponding positions of each picture;
3-7) fusing the fused approximate layer sub-image and the fused detail layer sub-image to obtain a fused S-channel image, wherein the fusion can be performed in a direct addition mode.
Fourth step: h channel fusion
The H channel contains tone information, gaussian filtering is firstly carried out on the tone information, the influence of noise is reduced, and then absolute value maximization fusion is selected.
wherein ,(mH ,n H ) For H channel coordinate position, c H (m H ,n H ) Representing the pixel values of the H-channel after fusion, representing pixel values for five wavelength H-channel images at 380nm, 605nm, 630nm, 655nm, and 680nm, respectively.
Fifth step: v channel fusion
Because the complex background exists in the form of brightness information, in order to weaken the influence of the complex background on subsequent defect detection, the V channel discards images at three wavelengths of 380nm, 605nm and 630nm, only fuses images at two wavelengths of 655nm and 680nm, firstly carries out Gaussian filtering on the images to reduce the influence of noise, and then carries out selective absolute value large fusion.
wherein ,,(mV ,n V ) For the V-channel coordinate position, c V (m v ,n v ) Representing the pixel values of the fused V-channel, representing pixel values for two wavelength V-channel images at 655nm and 680nm, respectively.
Sixth step: HSV image conversion after fusion
And finally, fusing the fused V-channel image, the fused H-channel image and the fused S-channel image to obtain a fused HSV image, and converting the HSV image back to an RGB image to obtain a final fused lithium battery image.
Examples
The specific flowchart of the multi-resolution mixed weight fusion method of the multispectral lithium battery image in the embodiment is shown in fig. 1, and the method comprises the following steps:
a mixed weight multispectral fusion method of lithium battery images comprises the following steps:
the first step: multispectral lithium battery image acquisition:
according to the invention, five wavelengths of 380nm, 605nm, 630nm, 655nm and 680nm are selected as the fusion source images of the lithium battery to be fused, the reflection of the 380nm-505nm wave band images is strong, and certain surface information is reserved; the image with the wave band of 530nm-630nm has higher brightness and saturation, the image layering property is better under the two wavelengths of 605nm and 630nm, and the defect characteristics are most obvious; the 655nm-780nm wave band image contains lower energy, weaker brightness and most obvious image defect characteristics at two wavelengths of 655nm and 680 nm.
And a second step of: RGB conversion HSV
The commonly used color spaces are RGB, HSV and YCbCr, the invention selects the image quality evaluation index of SSIM (structural similarity) to measure the relation among three channels of each color space, and the comparison in the table 1 shows that the HSV space has the minimum similarity compared with the other two spaces, that is, the difference of the information contained in each channel is larger, so the HSV space is selected for the next fusion processing, the lithium battery multispectral images of different wave bands selected in the first step are converted into the HSV space from the RGB space, and then the H, S, V three-channel images are processed independently.
TABLE 1 comparison of structural similarity of three-channel images in the same color space
Colorimetry is the science of researching visual laws of colors, color measurement theory and technology, and is based on the colorimetry system specified by the same international protocol to perform physical measurement on colors, and the international commission on illumination (CIE) has established a color matching function in 1931:
wherein c (lambda) is a color matching function, and the numbers of the three primary colors of red, green and blue corresponding to the isoelectric spectral color with the wavelength lambda are recorded as r, g and b.
The RGB to wavelength conversion relationship is:
wherein x, y and z are chromaticity coordinates, and R, G, B is the color representing three channels of red, green and blue.
And a third step of: empirical wavelet decomposition of the S-channel image:
the S channel image is decomposed into a detail layer and an approximate layer by using empirical wavelet decomposition, the empirical wavelet decomposition is a self-adaptive method, a group of filters are generated according to the characteristics of the signal, and the generated filters are used for filtering the S channel image of the multispectral lithium battery to obtain the detail layer and the approximate layer sub-image.
Fourth step: generating JND model-based saliency weight for S-channel image
JND refers to the Human Visual System (HVS) minimum perceived difference and defines a perception threshold to guide perceived image quality metric masking. It is an important visual saliency cue for the measurement of image quality. The present invention defines a saliency weight J (x, y) using a nonlinear addable JND model that is capable of extracting pixels with good saturation and contrast in different spectral images.
wherein and />Is background brightness masking and image visible texture masking, K l,t (0<K l,t < 1) represents an overlap of masking effects; x, y represent chromaticity coordinates; i=1 to N, N is the number of input source image sequences, n=5.
The invention uses two different functions to model brightnessAnd average background brightness. One is the root equation for low background luminance (less than 127) and the other is the approximately linear equation for the higher 127 portion, defined as:
where I (x, y) is the intensity value and B (a, B) is the low pass weight filter.
Texture masking is typically determined by the local spatial gradient around the pixel. In order to obtain a more accurate JND estimation, the edge and non-edge regions should be well distinguished. The invention is defined taking into account edge differences, since the HVS draws more attention to the edge structure/>
wherein ,is a convolution descriptor, g k (x, y) is the kth directional high pass filter, defined as:
fifth step: generating saturation weights for multispectral lithium battery raw images
The saturation weight is considered to better preserve the saturation information of the source image sequence, so that the saturation of the fusion result is improved, the saturation weight is generated for the original image of the multispectral lithium battery, and the saturation weight can be calculated according to the standard deviation of R, G, B three channels:
sixth step: mixing weights and fusion rules
The capability of the 6-1 detail layer is smaller, defect information is contained, and the mixed weight generated by the saturation weight and the saliency weight is used for fusion; in order to obtain a mixed weight of the ith Zhang Yuan image sequence considering the saturation weight and the significance weight, the method combines the two weights and normalizes the two weights to obtain the mixed weight:
wherein Wi (x, y) represents the mixed weight, S i (x, y) represents saturation weight, J i (x, y) represents JND saliency weight.
6-2 fusion rules
Wherein R (x, y) represents the pixel value of the coordinate position of the fusion result, W i (x, y) represents a mixed weight, D i (x, y) represents a layer of detail layer pixel values of the empirical wavelet decomposition of the ith S-channel image, N being the number of input source image sequences.
Seventh step: the absolute value of the sub-images of the approximate layer is taken and fused
The approximate layer capacity occupies a relatively large area, reflects the characteristics of the whole texture structure, and selects the absolute value to take the large fusion strategy, so that the edge strength after fusion is high, and the texture is clear.
wherein ,cS (m S ,n S ) Representing the value of the pixel after the fusion, pixel values representing five wavelength approximation layer sub-images of 380nm, 605nm, 630nm, 655nm, and 680nm, respectively
And fusing the fused approximate layer sub-image and the fused detail layer sub-image to obtain a fused S-channel image.
Eighth step: h channel fusion
The H channel contains tone information, gaussian filtering is firstly carried out on the tone information, the influence of noise is reduced, and then absolute value maximization fusion is selected.
wherein ,cH (m H ,n H ) Representing the value of the pixel after the fusion, representing pixel values for five wavelength H-channel images at 380nm, 605nm, 630nm, 655nm, and 680nm, respectively.
Ninth step: v channel fusion
Because the complex background exists in the form of brightness information, in order to weaken the influence of the complex background on subsequent defect detection, the V channel discards images at three wavelengths of 380nm, 605nm and 630nm, only fuses images at two wavelengths of 655nm and 680nm, firstly carries out Gaussian filtering on the images to reduce the influence of noise, and then carries out selective absolute value large fusion
wherein ,cV (m, n) represents the pixel value of the V channel after fusion,representing pixel values for two wavelength V-channel images at 655nm and 680nm, respectively.
Tenth step: HSV image conversion after fusion
And finally, converting the fused HSV image back to an RGB image to obtain a final fused lithium battery image.
The invention analyzes the fusion result of the lithium battery image and measures the performance of the method from four indexes of edge strength, average gradient, image definition and information entropy. As can be seen from Table 2, the mixed weight multispectral fusion algorithm of the lithium battery image provided by the invention is higher than other methods in four index aspects compared with other methods, has higher values than the original five wavelengths, enhances the defect part while inhibiting the complex background, and accords with the identification of the human visual system on the defect region.
Table II, method index measurement Performance
Edge strength | Average gradient | Image definition | Information entropy | |
The algorithm of the invention | 55.651 | 5.183 | 7.771 | 7.189 |
Laplacian pyramid | 49.943 | 4.855 | 6.874 | 6.374 |
Gradient domain method | 53.881 | 5.180 | 7.619 | 6.693 |
380nm | 26.966 | 2.726 | 3.311 | 5.625 |
605nm | 55.343 | 5.151 | 7.153 | 6.869 |
630nm | 50.325 | 4.849 | 6.161 | 6.869 |
655nm | 34.896 | 3.352 | 3.905 | 5.951 |
680nm | 34.957 | 3.358 | 3.913 | 5.955 |
The mixed weight multispectral fusion algorithm of the lithium battery image can meet the requirement of detecting the subsequent defects on the surface of the electrode of the lithium battery in the actual lithium battery industrial production, improve the accurate extraction efficiency of the surface defects, effectively prevent unqualified lithium batteries from flowing into the subsequent links, and further improve the quality and efficiency of quality inspection, and further improve the quality and production efficiency of the lithium battery module.
While the present invention has been described with reference to the above-described embodiments, it is to be understood that the above-described embodiments are illustrative only and not limiting, and that many forms may be made by those skilled in the art without departing from the spirit of the invention and the scope of the appended claims, which are to be construed as embodying the present invention.
The invention is applicable to the prior art where it is not described.
Claims (3)
1. A mixed weight multispectral fusion method of lithium battery images comprises the following steps:
the first step: multispectral lithium battery image acquisition:
respectively acquiring images of the same lithium battery in 380nm-505nm wave bands, 530nm-630nm wave bands and 655nm-780nm wave bands to form a multispectral RGB battery source image, and acquiring at least one image of the lithium battery with corresponding wavelength in each wave band;
and a second step of: RGB conversion HSV:
converting the multispectral RGB battery source images with different wave bands selected in the first step from RGB space to HSV space, and then respectively processing H, S, V three-channel images independently;
the conversion relation from RGB to HSV is as follows:
wherein X, Y, Z is chromaticity coordinates, R, G, B is the color representing three channels of red, green and blue;
and a third step of: fusing the S channel images:
3-1) decomposing the S channel image obtained in the second step into a detail layer sub-image and an approximate layer sub-image by using empirical wavelet decomposition;
3-2) generating JND model-based saliency weights
Defining significance weights J (x, y) for the S channel images obtained in the second step by using a nonlinear additive JND model, and extracting pixels with good saturation and contrast in different spectrum images;
wherein and />Is background brightness masking and image visible texture masking, K l,t Representing overlap of masking effects, 0 < K l,t < 1; x, y represent chromaticity coordinates; i=1 to N, N being the number of input source image sequences;
wherein I (x, y) is an intensity value, B (a, B) is a low-pass weight filter, and (a, B) represents the coordinate position of the low-pass weight filter B;
wherein ,is a convolution descriptor, g k (x, y) is the kth directional high pass filter, defined as:
3-3) generating saturation weight for the multispectral RGB battery source image obtained in the first step, and calculating saturation weight S according to standard deviation of R, G, B three channels i (x,y):
3-4) combining the saturation weight and the significance weight and normalizing the two to obtain a mixed weight W i (x, y), specifically defined as:
wherein Si (x, y) represents saturation weight, J i (x, y) represents JND saliency weight;
3-5) fusing the detail layer sub-images obtained in the step 3-1) by using a mixed weight according to the following rule to obtain fused detail layer sub-images,
wherein R (x, y) represents the pixel value of the coordinate position of the mixed weight fusion result, D i (x, y) represents a layer of detail layer pixel values of the empirical wavelet decomposition of the ith S-channel image;
3-6) carrying out absolute value large fusion on the approximate layer sub-image obtained in the step 3-1) to obtain a fused approximate layer sub-image;
3-7) fusing the fused approximate layer sub-image and the fused detail layer sub-image to obtain a fused S-channel image;
fourth step: fusion of H-channel images:
the H channel image contains tone information, gaussian filtering is firstly carried out on the H channel image to reduce the influence of noise, and then absolute value large fusion is carried out to obtain a fused H channel image;
fifth step: fusion of V-channel images:
discarding V channel images in 380nm-505nm wave bands and 530nm-630nm wave bands, only fusing V channel images in 655nm-780nm wave bands, firstly performing Gaussian filtering on the V channel images to reduce the influence of noise, and then performing selective absolute value large fusion to obtain fused V channel images;
sixth step: HSV image conversion after fusion:
and finally, fusing the fused V-channel image, the fused H-channel image and the fused S-channel image to obtain a fused HSV image, and converting the HSV image back to an RGB image to obtain a final fused lithium battery image.
2. The method of claim 1, wherein in the first step, the lithium battery image is selected at five wavelengths of 380nm, 605nm, 630nm, 655nm and 680 nm; n=5.
3. The method for multi-spectral fusion of mixed weights of lithium battery images according to claim 2, wherein the rule of the absolute value maximization fusion in the step 3-6) is as follows:
wherein ,(mS ,n S ) For the S-channel coordinate position, c S (m S ,n S ) Representing the pixel values after the approximate layer fusion, respectively represent 380nFive wavelength approximations of the sub-images at m, 605nm, 630nm, 655nm and 680nm,
the rule of the selective absolute value large fusion in the fifth step is as follows:
wherein ,(mV ,n V ) For the V-channel coordinate position, c V (m v ,n v ) Representing the pixel values of the fused V-channel, pixel values representing two wavelength V-channel images of 655nm and 680nm, respectively;
the rule of the absolute value large fusion in the fourth step is as follows:
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