CN114612345B - Light source detection method based on image processing - Google Patents
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Abstract
The invention relates to a light source detection method based on image processing; acquiring gray images corresponding to the photographed object under different degrees of light sources; dividing each gray image into a plurality of gray image blocks, and calculating the mutation degree of each pixel point in each gray image block so as to obtain estimated noise points in each gray image block; replacing the gray value of the estimated noise point by using the gray replacement value to obtain estimated image blocks corresponding to the gray image blocks; obtaining singular value threshold values corresponding to the gray image blocks; denoising operation is carried out on each gray image block based on the singular value threshold value, and denoising image blocks corresponding to each gray image block are obtained; splicing the denoising image blocks to obtain denoising images corresponding to the gray level images; and judging the number of noise points in each gray level image according to the denoising image and the corresponding gray level image, calculating the judgment index corresponding to each image information according to the number of noise points, and judging the light source corresponding to the maximum judgment index as the optimal light source. The invention can accurately detect the light source.
Description
Technical Field
The invention relates to the field of image processing, in particular to a light source detection method based on image processing.
Background
In the process of shooting or video shooting, the shot object is polished to improve the brightness and contrast of the image, reduce noise points in the image and enable a final imaging result to be better, wherein the imaging effects of the shot object under the light sources with different intensities are different.
In the preliminary preparation work of photographing, a photographer usually samples a sample, and the photographer detects whether the corresponding light source is an optimal light source according to photographing experience, that is, whether an image photographed under the light source is an image with the least noise points; however, the detection result is random, i.e., the accuracy of each detection result cannot be ensured; therefore, it is desirable to provide an accurate light source detection method.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a light source detection method based on image processing, which adopts the following technical scheme:
acquiring image information corresponding to a shot object under light sources of different degrees, and carrying out graying treatment on the image information to obtain a gray image;
dividing each gray image into a plurality of gray image blocks with m multiplied by m, calculating the mutation degree of each pixel point in each gray image block, and marking the pixel points corresponding to the mutation degree larger than a noise threshold as estimated noise points;
replacing gray values of estimated noise points in the gray image blocks with gray replacement values to obtain estimated image blocks corresponding to the gray image blocks;
acquiring singular value thresholds corresponding to the gray image blocks according to the gray image blocks and the corresponding estimated image blocks;
performing singular value decomposition denoising operation on each gray image block based on the singular value threshold to obtain a denoising image block corresponding to each gray image block;
splicing the denoising image blocks to obtain denoising images corresponding to the gray level images;
judging the number of noise points in each gray level image according to the denoising image and the corresponding gray level image; and calculating judgment indexes corresponding to the image information according to the number of the noise points, wherein the light source corresponding to the maximum judgment index is the optimal light source.
Further, the mutation degree is:
wherein ,δi To be the degree of mutation of pixel point i,
the gray value of the pixel point i under the light source of the v-th degree; />
For the gray value of the pixel point t under the light source of the v-th degree, the pixel point t is positioned right above the pixel point i; />
For the gray value of the pixel point u under the light source of the v-th degree, the pixel point u is positioned right below the pixel point i; />
For the gray value of the pixel point l under the light source of the v-th degree, the pixel point l is positioned at the left side of the pixel point i; />
For the gray value of the pixel point r under the light source of the v-th degree, the pixel point r is positioned right to the pixel point i.
Further, the gray scale replacement value includes a first replacement value and a second replacement value; obtaining a first coefficient according to the difference value between the gray value of the estimated noise point and the first replacement value; obtaining a second coefficient according to the difference value between the gray value of the estimated noise point and the second replacement value; comparing the first coefficient with the second coefficient, if the first coefficient is larger than the second coefficient, the first replacement value is the gray level replacement value of the estimated noise point, and if the first coefficient is smaller than the second coefficient, the second replacement value is the gray level replacement value of the estimated noise point.
Further, the first replacement value is:
wherein ,
in order to estimate the gray value of the noise point o under the light source of the v-th degree, N is the total number of pixel points in the gray image, < >>
In order to obtain the gray value of the pixel point corresponding to the estimated noise point o position in the c Zhang Huidu image under the light source of the v-th degree, j is the total number of gray images obtained under the light source of the v-th degree.
Further, the second replacement value is:
wherein ,
for estimating the gray value of the noise point o under the light source of the v-th degree, +.>
For the gray value of the pixel point a under the light source of the v-th degree, the pixel point a is positioned right above the estimated noise point o; />
For the gray value of the pixel point b under the light source of the v-th degree, the pixel point b is positioned right below the estimated noise point o; />
For the gray value of the pixel point e under the light source of the v-th degree, the pixel point e is positioned at the left side of the estimated noise point o; />
For the gray value of the pixel point f under the light source of the v-th degree, the pixel point f is located right to the estimated noise point o.
Further, the singular value threshold obtaining method comprises the following steps: calculating noise variances of the gray image blocks and the corresponding estimated image blocks, and determining a singular value threshold according to the noise variances;
the noise variance is:
wherein ,τ2 As the variance of the noise is the value of the variance of the noise,
for the matrix corresponding to the gray image block, A x In order to estimate the matrix to which the image block corresponds,
is the Frobenius norm.
Further, the method for judging the noise point comprises the following steps: calculating the absolute value of the difference value between the gray value of each pixel point in the denoising image and the gray value of each corresponding pixel point in the gray image, obtaining a difference image, judging the gray value of each pixel point in the difference image and the magnitude of a threshold value, and marking the pixel point with the gray value larger than the threshold value as a noise point.
Further, the threshold value obtaining method comprises the following steps:
wherein ,/>
in the formula ,hg In order to estimate the difference between the gray value of the noise point and the gray value of the corresponding pixel point in the denoising image, w is the number of the estimated noise points.
The embodiment of the invention has at least the following beneficial effects:
according to the method, firstly, singular value threshold values corresponding to all gray image blocks are calculated through estimated image blocks, secondly, singular value decomposition denoising operation is carried out on all gray image blocks according to the singular value threshold values to obtain denoising image blocks, further denoising images corresponding to gray images are obtained, then the number of noise points is calculated according to the denoising images and the corresponding gray images, finally, judging indexes corresponding to all gray images are calculated according to the number of the noise points, and a light source corresponding to the maximum judging index is an optimal light source. When the estimated image block is calculated, the gray value of the estimated noise point is replaced by the first replacement value and the second replacement value, so that the replacement result is more accurate, the obtained estimated image block is more accurate, and the accuracy of the detection result of the light source is higher.
According to the invention, through processing the gray images under the light sources with different degrees, the detection result of the corresponding light source is more accurate, the problem of randomness of the detection result in the prior art is solved, and a large amount of labor cost is saved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for detecting a light source based on image processing according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following detailed description refers to specific embodiments, structures, features and effects of a light source detection method based on image processing according to the present invention with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Referring to fig. 1, a flowchart of steps of a light source detection method based on image processing according to an embodiment of the present invention is shown, and the method includes the following steps:
step 1, obtaining corresponding image information of a shot object under light sources with different degrees, and carrying out graying treatment on the image information to obtain a gray image.
Specifically, light sources with different degrees are arranged to shine a shooting object, and a camera is used for collecting image information; wherein, the ratio of the long-side pixels to the wide-side pixels in the control image information is 1:1.
In this embodiment, the image information is subjected to the gradation processing by the weighted average method to obtain a gradation image, and as other embodiments, a maximum value method, a component method, an average value method, or the like may be used.
And 2, dividing each gray image into a plurality of gray image blocks with the size of m multiplied by m, calculating the mutation degree of each pixel point in each gray image block, and recording the pixel points corresponding to the mutation degree larger than the noise threshold as estimated noise points.
The mutation degree is as follows:
wherein ,δi To be the degree of mutation of pixel point i,
the gray value of the pixel point i under the light source of the v-th degree; />
For the gray value of the pixel point t under the light source of the v-th degree, the pixel point t is positioned right above the pixel point i; />
For the gray value of the pixel point u under the light source of the v-th degree, the pixel point u is positioned right below the pixel point i; />
For the gray value of the pixel point l under the light source of the v-th degree, the pixel point l is positioned at the left side of the pixel point i; />
For the gray value of the pixel point r under the light source of the v-th degree, the pixel point r is positioned right to the pixel point i.
In this embodiment, the noise threshold is set to 200, and in a specific operation, the noise threshold can be adjusted by an implementer according to actual situations. The greater the degree of mutation of a pixel, the greater the likelihood that the pixel is an estimated noise point.
Specifically, the size of the gray image block is 1/10 of the size of the gray image, and in the actual operation process, an implementer can adjust the size of the gray image block according to the situation; the size of the gray image block influences the denoising precision and the calculated amount in the subsequent singular value decomposition denoising process, and the excessive gray image block can cause too few similar blocks searched in the singular value decomposition denoising process, so that the denoising precision is reduced; the gray image blocks are too small, so that the number of similar blocks found in the singular value decomposition denoising process is too large, the calculated amount is greatly increased, and the denoising result is inaccurate, so that proper gray image blocks need to be selected.
The grayscale image blocks in this embodiment include grayscale image blocks containing estimated noise points and grayscale image blocks not containing estimated noise points.
And 3, replacing the gray value of the estimated noise point in the gray image block by using the gray replacement value to obtain an estimated image block corresponding to each gray image block.
The gray level replacement values include a first replacement value and a second replacement value; obtaining a first coefficient according to the difference value between the gray value of the estimated noise point and the first replacement value; obtaining a second coefficient according to the difference value between the gray value of the estimated noise point and the second replacement value; comparing the first coefficient with the second coefficient, if the first coefficient is larger than the second coefficient, the first replacement value is the gray level replacement value of the estimated noise point, and if the first coefficient is smaller than the second coefficient, the second replacement value is the gray level replacement value of the estimated noise point o.
The first replacement value is:
wherein ,
in order to estimate the gray value of the noise point o under the light source of the v-th degree, N is the total number of pixel points in the gray image, < >>
In order to obtain the gray value of the pixel point corresponding to the estimated noise point o position in the c Zhang Huidu image under the light source of the v-th degree, j is the total number of gray images obtained under the light source of the v-th degree.
It should be noted that, the noise related to the illumination is mainly poisson noise, so the noise considered in this embodiment is poisson noise, and because poisson noise satisfies poisson distribution, that is, the positions of certain noise points in multiple images obtained under the same degree of light source are not kept unchanged all the time, if the pixel point at the coordinates (x, y) in one image is the noise point, the pixel point at the coordinates (x, y) in the rest of other images is not the noise point, this embodiment uses this characteristic of poisson noise to determine the first replacement value
Representing the probability of a pixel point with coordinates (x, y) as a noise point in one of the images, < >>
The probability that the pixel point with coordinates (x, y) in the remaining other images is a non-noise point is represented.
The second replacement value is:
wherein ,
for estimating the gray value of the noise point o under the light source of the v-th degree, +.>
For the gray value of the pixel point a under the light source of the v-th degree, the pixel point a is positioned right above the estimated noise point o; />
For the gray value of the pixel point b under the light source of the v-th degree, the pixel point b is positioned right below the estimated noise point o; />
For the gray value of the pixel point e under the light source of the v-th degree, the pixel point e is positioned at the left side of the estimated noise point o; />
For the gray value of the pixel point f under the light source of the v-th degree, the pixel point f is located right to the estimated noise point o.
In this embodiment, if other estimated noise points exist in the 4 neighboring pixel points of the estimated noise point o, but other estimated noise points are not screened out due to selection of the noise threshold, at this time, the calculated second replacement value becomes larger, and further the second coefficient becomes smaller, so that the replacement value corresponding to the larger coefficient is selected as the gray level replacement value of the estimated noise point o, thereby reducing errors and enabling the replacement result of the estimated noise point o to be more accurate.
And 4, acquiring singular value thresholds corresponding to the gray image blocks according to the gray image blocks and the corresponding estimated image blocks.
Specifically, the singular value threshold obtaining method includes: and calculating the noise variance of the gray image block and the corresponding estimated image block, and determining a singular value threshold according to the noise variance.
The noise variance is:
wherein ,τ2 As the variance of the noise is the value of the variance of the noise,
for the matrix corresponding to the gray image block, A x In order to estimate the matrix to which the image block corresponds,
is the Frobenius norm.
Further, it is known from the well-known theorem that: for any real matrix Y, if the rank of matrix X is k, then there is:
wherein ,
is the Frobenius norm lambda i (i=1, 2,3 … n) is a singular value of matrix Y.
Therefore, the relational expression obtained by the above theorem is:
if and only if
When this formula equation holds. A is that 0 For denoising image block->
Matrix corresponding to gray image block>
After all return to 0 after the kth singular value, inverse transformed matrix; thus (S)>
Can be approximated as a denoised image block, based on which the noise variance τ is solved 2 And->
And determining the value of k as the singular value threshold of the gray image block corresponding to the estimated noise point.
In the embodiment, the gray image blocks including the estimated noise point have the corresponding estimated image blocks and the singular value threshold, and the gray image blocks not including the estimated noise point have the no corresponding estimated image blocks and the singular value threshold.
And 5, performing singular value decomposition denoising operation on each gray image block based on the singular value threshold value to obtain a denoising image block corresponding to each gray image block.
In the embodiment, before performing singular value decomposition denoising, searching similar blocks corresponding to each gray image block by using a block matching algorithm; the conditions for the similar blocks are: firstly, marking the gray value of an estimated noise point as 0 in a matrix corresponding to a gray image block, recording the position to obtain a new matrix, then carrying out global search on a gray image to find out a matrix with the gray value unequal to the gray value at the 0 position and the gray values equal to the gray values at other positions in the new matrix, and judging that the corresponding image block of the matrix is a similar block of the gray image block; the block matching algorithm is a well-known technique, and is not repeated here.
Specifically, the singular value decomposition denoising process is as follows: matrix corresponding to gray image block
Singular value decomposition, i.e.)>
wherein ,U,VT Is an orthogonal matrix, Λ is +.>
The singular values are arranged from big to small in the singular value matrix, and the singular values obtained in the step 4 in the singular value matrix are thresholdedThe singular values after the value k are all normalized to 0, and then the obtained
Matrix denoised by singular value decomposition ∈>
Similarly, the same operation is performed on the matrices corresponding to similar blocks, resulting in a matrix set +.>
(n=1, 2,3 … n,) n is the total number of similar blocks; the matrix of the gray image block corresponding to the denoised image block is:
in the formula ,A0 In order to denoise the matrix corresponding to the image block,
is->
Matrix after singular value decomposition denoising, < > is performed>
And (3) performing singular value decomposition on the matrix corresponding to the z-th similar block to remove noise.
In step 4 of the present embodiment, it has been pointed out that the gray image block without the estimated noise point has no corresponding singular value threshold, and therefore, the gray image block without the estimated noise point does not need to perform singular value decomposition denoising operation; the present embodiment takes a gray image block containing no estimated noise point as its corresponding denoised image block.
It should be noted that, in this embodiment, only the gray level of the estimated noise point is replaced by the gray level replacement value to obtain the estimated image block, so that the singular value threshold is determined, and no adaptive analysis is performed on the singular value threshold, so that a part of noise points are not detected by the singular value threshold, and thus, the phenomenon that the denoising image block is inaccurate occurs, so that the denoising image block can be obtained more accurately through the selection of the similar block.
And 6, splicing the denoising image blocks to obtain denoising images corresponding to the gray level images.
Specifically, the splicing method comprises the following steps: and finding out each denoising image block corresponding to each gray level image, and splicing each denoising image block according to the coordinate positions to obtain the denoising image corresponding to the gray level image.
Step 7, judging the number of noise points in each gray level image according to the denoising image and the corresponding gray level image; and calculating the judging index corresponding to each gray level image according to the number of the noise points, wherein the light source corresponding to the maximum judging index is the optimal light source.
The judgment method of the noise point comprises the following steps: and calculating the absolute value of the difference value between the gray value of each pixel point in the denoising image and the gray value of each corresponding pixel point in the gray image, obtaining a difference image, judging the gray value of each pixel point in the difference image and the magnitude of a threshold value, and marking the pixel point with the gray value larger than the threshold value as a noise point.
The threshold value in the above is:
wherein ,/>
in the formula ,hg In order to estimate the difference between the gray value of the noise point and the gray value of the corresponding pixel point in the denoising image, e is the number of the estimated noise points.
Further, in order to obtain the number of final noise points in the gray image more clearly, the gray value of the final noise point is marked as 255, and the gray value of the non-final noise point is marked as 0; and further obtaining a binary image corresponding to the difference image. In actual operation, the practitioner may note the gray value of the final noise point as any integer value from 1 to 255.
Specifically, the method for acquiring the judgment index comprises the following steps: and calculating the ratio of the number of noise points to the total number of pixel points in the corresponding gray level image to obtain the duty ratio of the noise points, and obtaining the judgment index corresponding to each image information according to the sum of the inverse of the duty ratio of the noise points and the signal-to-noise ratio of the corresponding image information.
The signal-to-noise ratio reflects the noise power of a graph in the image, and the larger the illumination intensity is, the larger the signal-to-noise ratio is, and the fewer the number of noise points is; the larger the signal-to-noise ratio is, the larger the judgment index is, the smaller the signal-to-noise ratio is, and the smaller the judgment index is.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (8)
1. The light source detection method based on image processing is characterized by comprising the following steps:
acquiring image information corresponding to a shot object under light sources of different degrees, and carrying out graying treatment on the image information to obtain a gray image;
dividing each gray image into a plurality of gray image blocks with m multiplied by m, calculating the mutation degree of each pixel point in each gray image block, and marking the pixel points corresponding to the mutation degree larger than a noise threshold as estimated noise points;
replacing gray values of estimated noise points in the gray image blocks with gray replacement values to obtain estimated image blocks corresponding to the gray image blocks;
acquiring singular value thresholds corresponding to the gray image blocks according to the gray image blocks and the corresponding estimated image blocks;
performing singular value decomposition denoising operation on each gray image block based on the singular values to obtain denoising image blocks corresponding to each gray image block;
splicing the denoising image blocks to obtain denoising images corresponding to the gray level images;
judging the number of noise points in each gray level image according to the denoising image and the corresponding gray level image; and calculating the ratio of the number of noise points to the total number of pixel points in the corresponding gray level image according to the number of noise points to obtain the noise point duty ratio, and obtaining the judgment index corresponding to each image information according to the sum of the inverse of the noise point duty ratio and the signal to noise ratio of the corresponding image information, wherein the light source corresponding to the maximum judgment index is the optimal light source.
2. The method for detecting a light source based on image processing according to claim 1, wherein,
the mutation degree is as follows:
wherein ,δi To be the degree of mutation of pixel point i,
the gray value of the pixel point i under the light source of the v-th degree; />
For the gray value of the pixel point t under the light source of the v-th degree, the pixel point t is positioned right above the pixel point i; />
For the gray value of the pixel point u under the light source of the v-th degree, the pixel point u is positioned right below the pixel point i; />
For the gray value of the pixel point l under the light source of the v-th degree, the pixel point l is positioned at the left side of the pixel point i; />
For the gray value of the pixel point r under the light source of the v-th degree, the pixel point r is positioned right to the pixel point i.
3. The method for detecting a light source based on image processing according to claim 1, wherein,
the gray scale replacement value comprises a first replacement value and a second replacement value; obtaining a first coefficient according to the difference value between the gray value of the estimated noise point and the first replacement value; obtaining a second coefficient according to the difference value between the gray value of the estimated noise point and the second replacement value; comparing the first coefficient with the second coefficient, if the first coefficient is larger than the second coefficient, the first replacement value is the gray level replacement value of the estimated noise point, and if the first coefficient is smaller than the second coefficient, the second replacement value is the gray level replacement value of the estimated noise point.
4. A light source detection method based on image processing according to claim 3, wherein,
the first replacement value is:
wherein ,
In order to estimate the gray value of the noise point o under the light source of the v-th degree, N is the total number of pixel points in the gray image, < >>
In order to obtain the gray value of the pixel point corresponding to the estimated noise point o position in the c Zhang Huidu image under the light source of the v-th degree, j is the total number of gray images obtained under the light source of the v-th degree. />
5. A light source detection method based on image processing according to claim 3, wherein,
the second replacement value is:
wherein ,
for estimating the gray value of the noise point o under the light source of the v-th degree, +.>
For the gray value of the pixel point a under the light source of the v-th degree, the pixel point a is positioned right above the estimated noise point o; />
For the gray value of the pixel point b under the light source of the v-th degree, the pixel point b is positioned right below the estimated noise point o; />
For the gray value of the pixel e under the light source of the v-th degree, the pixel e is positioned at the estimated noise oLeft; />
For the gray value of the pixel point f under the light source of the v-th degree, the pixel point f is located right to the estimated noise point o.
6. The method for detecting a light source based on image processing according to claim 1, wherein the method for obtaining the singular value threshold is as follows: calculating noise variances of the gray image blocks and the corresponding estimated image blocks, and determining a singular value threshold according to the noise variances;
the noise variance is:
wherein ,τ2 As the variance of the noise is the value of the variance of the noise,
for the matrix corresponding to the gray image block, A x For estimating the matrix corresponding to the image block, +.>
Is the Frobenius norm.
7. The method for detecting a light source based on image processing according to claim 1, wherein the method for determining the noise point is as follows: calculating the absolute value of the difference value between the gray value of each pixel point in the denoising image and the gray value of each corresponding pixel point in the gray image, obtaining a difference image, judging the gray value of each pixel point in the difference image and the magnitude of a threshold value, and marking the pixel point with the gray value larger than the threshold value as a noise point.
8. The method for detecting a light source based on image processing according to claim 1, wherein the method for obtaining the threshold value is as follows:
wherein ,/>
in the formula ,hg In order to estimate the difference between the gray value of the noise point and the gray value of the corresponding pixel point in the denoising image, w is the number of the estimated noise points. />
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