CN111932478A - Self-adaptive non-uniform correction method for uncooled infrared focal plane - Google Patents
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Abstract
The invention discloses a self-adaptive non-uniform correction method of an uncooled infrared focal plane, aiming at the characteristic of non-uniformity of the uncooled infrared focal plane and the defect of shutter correction, introducing the non-uniformity of each scale of a Gaussian pyramid time domain histogram focusing plane for mean value correction to achieve the purpose of correction; in addition, in order to eliminate the phenomena of artifacts and image edge degradation caused by correction, a distance factor and gray factor calculation mechanism is added in time domain histogram statistics, and the ghost caused by correction is effectively eliminated.
Description
Technical Field
The invention belongs to the technical field of infrared focal planes, and particularly relates to a self-adaptive non-uniform correction method of an uncooled infrared focal plane.
Background
The infrared focal plane array belongs to the infrared optical system focal plane, and can make each pixel in the whole visual scene correspond to a light and shade element. The nonuniformity of the infrared focal plane array can change along with the changes of the using environment temperature, the imaging target temperature, the self temperature of the focal plane and the working time of the focal plane, and the correction coefficient needs to be updated in real time. Aiming at the characteristic of non-uniformity of the uncooled infrared focal plane and the defect of shutter correction, a self-adaptive correction method of the uncooled infrared focal plane is expected, and the defects in the prior art can be effectively overcome.
Disclosure of Invention
Aiming at the defects in the prior art, the self-adaptive non-uniform correction method for the uncooled infrared focal plane provided by the invention corrects the non-uniformity of each scale of the infrared focal plane.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a self-adaptive non-uniform correction method for an uncooled infrared focal plane comprises the following steps:
s1, carrying out Gaussian pyramid model decomposition on the original infrared image acquired by the uncooled infrared focal plane to obtain a single-layer Gaussian pyramid image under each scale;
s2, respectively carrying out non-uniform correction on the histogram of the Gaussian pyramid image under each scale to obtain corrected sub-images under each scale;
and S3, reconstructing the corrected sub-images by using a Gaussian pyramid to finish the correction of the original infrared images, thereby realizing the correction of the uncooled infrared focal plane.
Further, the step S1 is specifically:
and smoothing the original infrared image by a low-pass filter, and then sampling the smoothed infrared image to obtain a single-layer Gaussian pyramid image under a corresponding scale.
Further, when the smoothed infrared image is sampled, each level of the sampled image is obtained by performing interlaced alternate downsampling after the previous level of the image is subjected to low-pass filtering, namely:
in the formula, Gk(X, Y) is a k-th layer gaussian pyramid image, subscript k is the scale number of the gaussian pyramid image, k is 1,2, 3. w (m, n) ═ h (m) · h (n) is a 3 × 3 window function with low-pass characteristics; h (-) is a Gaussian density distribution function; subscript (m, n) is coordinate value of Gaussian window, (x, y) is pixel coordinate in Gaussian pyramid image; when k is 0, Go(. cndot.) is an original infrared image,Go(·)、G1(·)、G2(·)、...、GN(. cndot.) constitutes a gaussian pyramid.
Further, the method for performing non-uniformity correction on the histogram of the single gaussian pyramid image in step S2 specifically includes:
a1, setting the gray scale of the image element imaged by the infrared focal plane as continuous, and setting the response density function rho of the corresponding detection unitm(y) is defined as the mean of the neighborhood pixel response density function;
a2, calculating the current response density function rhomAnd (y) taking the mean value of the histograms formed by the response values of all the pixels in the single-layer Gaussian pyramid image corresponding to the (y) as a new histogram h (y) of each pixel (i, j) in the single-layer Gaussian pyramid image, and further realizing the non-uniform correction of the histograms of the Gaussian pyramid images respectively.
Further, the response density function ρ of the detection unitm(y) is:
in the formula, N is the total number of neighborhoods of the current detection unit, subscripts i and j are the position coordinates of the neighborhood pixels (i and j), W is the total number of horizontal and vertical coordinates of the neighborhood pixels, and rhoijAnd (y) is a neighborhood pixel response density function.
Further, the histogram h (y) is:
in the formula, hijAnd (y) is a histogram of the neighborhood pixels (i, j), and W is the size of the histogram.
Further, in the step a2, when determining the histogram h (y), the target marginalization and the artifacts of the corresponding single-layer gaussian pyramid image are eliminated by adding the spatial distance factor and the gray scale similarity factor, so as to obtain:
in the formula, ws(i, j) is a spatial distance factor, wr(i, j) is a gray scale similarity factor, wherein,
f (i, j) is the gray value of the pixel (i, j), f (k, l) is the gray value of the pixel with the central point being (k, l), σr 2In order to be the variance of the degree of similarity,
σs 2is the distance variance.
The invention has the beneficial effects that:
aiming at the characteristic of non-uniformity of the non-refrigeration infrared focal plane and the defect of shutter correction, the self-adaptive non-uniform correction method of the non-refrigeration infrared focal plane introduces Gaussian pyramid time domain histogram to perform mean value correction on all scale non-uniformities of the focal plane so as to achieve the purpose of correction; in addition, in order to eliminate the phenomena of artifacts and image edge degradation caused by correction, a distance factor and gray factor calculation mechanism is added in time domain histogram statistics, and ghosting caused by correction is effectively eliminated.
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FIG. 1 is a flow chart of a method for adaptive non-uniformity correction of an uncooled infrared focal plane.
Fig. 2 is an original infrared image in an embodiment provided by the present invention.
Fig. 3 is an image corrected by a two-point correction method according to an embodiment of the present invention.
FIG. 4 is an image that is generally corrected by the method of the present invention according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Example 1:
the non-uniform correction linear model of the infrared focal plane pixel can be written as:
Yk(i,j)＝ak(i,j)*Xk(i,j)-bk(i,j) (1)
in the formula, Yk(i, j) is the pixel output gray response value, ak(i, j) and bk(i, j) is the gain and offset coefficient of the pixel (i, j), Xk(i, j) is the input radiant energy of the pixel;
if the imaging scene keeps relative motion, input radiation can be regarded as random variables, the chances that each pixel receives radiant energy with various energies are equal, considering that the practical application is difficult to keep the focal plane and the imaging scene always in random motion, the assumption is made that the chances that the pixel receives radiant energy in a local space are equal, an input density function is defined as f (x), and the probability density function approximate expression of the pixel output response value obtained by the formula (1) is as follows:
ρij(y)≈ak(i,j)f(x)
in the formula, ρijAnd (y) represents a response density function, and as can be seen from equation (2), the non-uniformity correction is realized for a long time as the response density function of each pixel output is converted into the form of the same density function.
Based on the non-uniformity correction principle, as shown in fig. 1, the present embodiment provides an adaptive non-uniformity correction method for an uncooled infrared focal plane, which includes the following steps:
s1, carrying out Gaussian pyramid model decomposition on the original infrared image acquired by the uncooled infrared focal plane to obtain a single-layer Gaussian pyramid image under each scale;
s2, respectively carrying out non-uniform correction on the histogram of the Gaussian pyramid image under each scale to obtain corrected sub-images under each scale;
and S3, reconstructing the corrected sub-images by using a Gaussian pyramid to finish the correction of the original infrared images, thereby realizing the correction of the uncooled infrared focal plane.
In step S1 of this embodiment, the non-uniformity of the original infrared image at different scales may show different forms, and in the case of the conventional non-uniform correction, the correction algorithm cannot accurately correct the target scale in the image, so that the non-uniform noise can be effectively filtered out and the image signal-to-noise ratio can be improved in the multi-scale non-uniform correction of the infrared image. The step S1 is specifically:
and smoothing the original infrared image by a low-pass filter, and then sampling the smoothed infrared image to obtain a single-layer Gaussian pyramid image under a corresponding scale.
When the smoothed infrared image is sampled, each level of the sampled image is obtained by performing interlaced alternate row downsampling after the previous level of the image is subjected to low-pass filtering, namely:
in the formula, Gk(X, Y) is a k-th layer gaussian pyramid image, subscript k is the scale number of the gaussian pyramid image, k is 1,2, 3. w (m, n) ═ h (m) · h (n) is a 3 × 3 window function with low-pass characteristics; h (-) is a Gaussian density distribution function; subscript (m, n) is coordinate value of Gaussian window, (x, y) is pixel coordinate in Gaussian pyramid image; when k is 0, Go(. is an original infrared image, G)o(·)、G1(·)、G2(·)、...、GN(. cndot.) constitutes a gaussian pyramid, where the window function w (m, n) can be expressed as:
in step S2 of the present embodiment, when the pixel levels of the infrared focal plane imaging are continuous, the average value of the difference between adjacent pixels in the infrared image is small over a certain number of frames, which means that the time-domain histograms of two adjacent pixels are almost equal. Based on this assumption, we specify the response density function of a single probe unit to the mean of the response density functions of the neighboring pixels, thereby achieving non-uniformity correction. Therefore, in step S2, the method for performing non-uniformity correction on the histogram of the single gaussian pyramid image specifically includes:
a1, setting the gray scale of the image element imaged by the infrared focal plane as continuous, and setting the response density function rho of the corresponding detection unitm(y) is defined as the mean of the neighborhood pel response density function,
a2, calculating the current response density function rhomAnd (y) taking the mean value of the histograms formed by the response values of all the pixels in the single-layer Gaussian pyramid image corresponding to the (y) as a new histogram h (y) of each pixel (i, j) in the single-layer Gaussian pyramid image, and further realizing the non-uniform correction of the histograms of the Gaussian pyramid images respectively.
Wherein the response density function ρ of the detection unitm(y) is:
in the formula, N is the total number of neighborhoods of the current detection unit, subscripts i and j are the position coordinates of the neighborhood pixels (i and j), W is the total number of horizontal and vertical coordinates of the neighborhood pixels, and rhoij(y) is a neighborhood pixel response density function;
the mean histogram h (y) formed by pixel element outputs is:
in the formula, hijAnd (y) is a histogram of the neighborhood pixels (i, j), and W is the size of the histogram.
It can be demonstrated that:
when the method is actually applied, the problem that the image edge has degradation when the scene is static is found, in the practical application, in an area with small gray difference, the time domain histograms of all the pixels in the neighborhood average can be well approximated to the pixel input radiation energy histogram, but for the image edge, the error of the approximation pixel input radiation histogram is large when the histograms of all the pixels in the neighborhood average are directly averaged, if the position of the image edge is continuously changed in the scene motion, the independent approximation error can not influence the histogram statistics, but when the scene is static, the error can be continuously accumulated, and finally the statistical accuracy of the mean value histogram is influenced. Therefore, in the step a2, when determining the histogram h (y), the spatial distance factor and the gray scale similarity factor are added to eliminate the target marginalization and the artifacts corresponding to the single-layer gaussian pyramid image, so as to obtain:
in the formula, ws(i, j) is the decrease of the spatial distance factor with increasing Euclidean distance between the center points, wr(i, j) is a gray scale similarity factor that decreases with increasing gray scale difference, wherein,
f (i, j) is the gray value of the pixel (i, j), f (k, l) is the gray value of the pixel with the central point (k, l), S represents the nine-neighborhood space with the central point (k, l), and sigma isr 2In order to be the variance of the degree of similarity,
σs 2is the distance variance.
Example 2:
the embodiment provides the comparison of the correction effect of the same original corrected image (figure 2) by the traditional two-point correction method and the method of the invention, wherein the correction effect graph obtained by the two-point correction method is shown in figure 3, the correction effect graph obtained by the method of the invention is shown in figure 4, and as can be seen from figures 3 and 4, compared with the two-point correction method, the algorithm has better correction precision, the fixed noise is obviously reduced after the image correction, and the image quality is improved.
Claims (7)
1. A self-adaptive non-uniform correction method of an uncooled infrared focal plane is characterized by comprising the following steps:
s1, carrying out Gaussian pyramid model decomposition on the original infrared image acquired by the uncooled infrared focal plane to obtain a single-layer Gaussian pyramid image under each scale;
s2, respectively carrying out non-uniform correction on the histogram of the Gaussian pyramid image under each scale to obtain corrected sub-images under each scale;
and S3, reconstructing the corrected sub-images by using a Gaussian pyramid to finish the correction of the original infrared images, thereby realizing the correction of the uncooled infrared focal plane.
2. The method for adaptive non-uniformity correction of an uncooled infrared focal plane according to claim 1, wherein the step S1 is specifically:
and smoothing the original infrared image by a low-pass filter, and then sampling the smoothed infrared image to obtain a single-layer Gaussian pyramid image under a corresponding scale.
3. The method of claim 2, wherein when sampling the smoothed infrared image, each level of the sampled image is obtained by performing low pass filtering on the previous level of the image and then performing interlaced downsampling, that is:
in the formula, Gk(X, Y) is the k-th layer gaussian pyramid image, subscript k is the scale number of the gaussian pyramid image, k is 1,2,3,.., N N is the total number of scales of gaussian pyramid images; w (m, n) ═ h (m) · h (n) is a 3 × 3 window function with low-pass characteristics; h (-) is a Gaussian density distribution function; subscript (m, n) is coordinate value of Gaussian window, (x, y) is pixel coordinate in Gaussian pyramid image; when k is 0, Go(. is an original infrared image, G)o(·)、G1(·)、G2(·)、...、GN(. cndot.) constitutes a gaussian pyramid.
4. The adaptive non-uniformity correction method for an uncooled infrared focal plane according to claim 1, wherein the method for performing non-uniformity correction on the histogram of the single gaussian pyramid image in the step S2 specifically comprises:
a1, setting the gray scale of the image element imaged by the infrared focal plane as continuous, and setting the response density function rho of the corresponding detection unitm(y) is defined as the mean of the neighborhood pixel response density function;
a2, calculating the current response density function rhomAnd (y) taking the mean value of the histograms formed by the response values of all the pixels in the single-layer Gaussian pyramid image corresponding to the (y) as a new histogram h (y) of each pixel (i, j) in the single-layer Gaussian pyramid image, and further realizing the non-uniform correction of the histograms of the Gaussian pyramid images respectively.
5. The method for adaptive non-uniformity correction of an uncooled infrared focal plane according to claim 4, wherein the response density function p of the detection unitm(y) is:
in the formula, N is the total number of neighborhoods of the current detection unit, subscripts i and j are the position coordinates of the neighborhood pixels (i and j), W is the total number of horizontal and vertical coordinates of the neighborhood pixels, and rhoijAnd (y) is a neighborhood pixel response density function.
6. The method of adaptive non-uniformity correction of an uncooled infrared focal plane according to claim 5, wherein the histogram h (y) is:
in the formula, hijAnd (y) is a histogram of the neighborhood pixels (i, j), and W is the size of the histogram.
7. The adaptive non-uniform correction method for an uncooled infrared focal plane according to claim 6, wherein in the step A2, when determining the histogram h (y), the spatial distance factor and the gray scale similarity factor are added to eliminate the target marginalization and the artifacts of the corresponding single-layer Gaussian pyramid image, so as to obtain:
in the formula, ws(i, j) is a spatial distance factor, wr(i, j) is a gray scale similarity factor, wherein,
f (i, j) is the gray value of the pixel (i, j), f (k, l) is the gray value of the pixel with the central point being (k, l), σr 2In order to be the variance of the degree of similarity,
σs 2is the distance variance.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112700382A (en) * | 2020-12-23 | 2021-04-23 | 杭州海康微影传感科技有限公司 | Image seam eliminating method and device and electronic equipment |
| CN113421202A (en) * | 2021-06-25 | 2021-09-21 | 浙江大华技术股份有限公司 | Method and device for correcting low-frequency heterogeneity of infrared image and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102163329A (en) * | 2011-03-15 | 2011-08-24 | 河海大学常州校区 | Super-resolution reconstruction method of single-width infrared image based on scale analogy |
| CN104599248A (en) * | 2015-01-06 | 2015-05-06 | 中国科学院西安光学精密机械研究所 | Heterogeneity correction method for matching of multi-scale time domain matrixes |
| CN104657945A (en) * | 2015-01-29 | 2015-05-27 | 南昌航空大学 | Infrared small target detection method for multi-scale spatio-temporal union filtering under complex background |
| CN104833426A (en) * | 2014-02-10 | 2015-08-12 | 上海蓝剑科技发展有限公司 | Scene adaptive infrared focal plane imaging system gray scale super-resolution method |
| US20160274520A1 (en) * | 2015-03-17 | 2016-09-22 | Takashi Soma | Image forming apparatus, image processing method, and computer-readable recording medium |
| US10657378B2 (en) * | 2015-09-25 | 2020-05-19 | Board Of Regents, The University Of Texas System | Classifying images and videos |
-
2020
- 2020-08-10 CN CN202010794345.4A patent/CN111932478A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102163329A (en) * | 2011-03-15 | 2011-08-24 | 河海大学常州校区 | Super-resolution reconstruction method of single-width infrared image based on scale analogy |
| CN104833426A (en) * | 2014-02-10 | 2015-08-12 | 上海蓝剑科技发展有限公司 | Scene adaptive infrared focal plane imaging system gray scale super-resolution method |
| CN104599248A (en) * | 2015-01-06 | 2015-05-06 | 中国科学院西安光学精密机械研究所 | Heterogeneity correction method for matching of multi-scale time domain matrixes |
| CN104657945A (en) * | 2015-01-29 | 2015-05-27 | 南昌航空大学 | Infrared small target detection method for multi-scale spatio-temporal union filtering under complex background |
| US20160274520A1 (en) * | 2015-03-17 | 2016-09-22 | Takashi Soma | Image forming apparatus, image processing method, and computer-readable recording medium |
| US10657378B2 (en) * | 2015-09-25 | 2020-05-19 | Board Of Regents, The University Of Texas System | Classifying images and videos |
Non-Patent Citations (2)
| Title |
|---|
| 吴炜 等: "《基于学习的图像增强技术》", 28 February 2013, 西安电子科技大学出版社 * |
| 贺明 等: "双边滤波直方图均衡的非均匀性校正算法", 《红外与激光工程》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112700382A (en) * | 2020-12-23 | 2021-04-23 | 杭州海康微影传感科技有限公司 | Image seam eliminating method and device and electronic equipment |
| CN112700382B (en) * | 2020-12-23 | 2024-03-26 | 杭州海康微影传感科技有限公司 | Image seam elimination method and device and electronic equipment |
| CN113421202A (en) * | 2021-06-25 | 2021-09-21 | 浙江大华技术股份有限公司 | Method and device for correcting low-frequency heterogeneity of infrared image and storage medium |
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