CN117553924A - Infrared image non-uniformity correction method and device based on statistical characteristics - Google Patents
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Abstract
The invention discloses an infrared image non-uniformity correction method and device based on statistical characteristics, wherein the correction method is based on rich pixel distribution brought by infrared images of multiple ground object scenes, firstly, the statistics is carried out on pixel points of an original infrared image, and the rearrangement is carried out according to the size of pixel values, so as to obtain uniformly-changed reorganized data; and finally, simulating a response curve of the detector by utilizing the linear correlation of multiple areas, thereby calculating the non-uniform correction coefficient of each pixel point. The invention can overcome the influence of the nonlinearity of the response of the detector, achieves the effect of correcting the nonlinearity of two different types in the infrared image simultaneously, has high calculation speed, good correction effect, good universality and self-adaptability, can effectively improve the imaging quality and definition of the image, and lays a foundation for the subsequent image analysis and application.
Description
Technical Field
The invention belongs to the field of infrared image correction, and particularly relates to an infrared image non-uniformity correction method and device based on statistical characteristics.
Background
The infrared imaging technology has wide application in the fields of ecological environment, disaster and emergency management and monitoring and the like. However, due to defects of the manufacturing materials and uncertainty of process control, response output of each probe element of the infrared detector to radiation input of the same uniform area is different, so that non-uniformity of an image is caused, and usability and interpretability of the infrared image are seriously affected.
Due to defects of manufacturing materials, processes and the like, an infrared image acquired by a detector is often affected by superposition of two non-uniform noises caused by a detector element and a readout circuit. In addition, the output response of the infrared detector, which is limited by the dynamic range of the circuit, shows strong nonlinearity at the low and high ends of the output signal.
In order to solve the above problems, researchers have proposed two types of methods, one is a calibration correction method of a reference standard radiation source, for example, chinese patent document publication No. CN102289788A discloses a real-time correction method of stripe non-uniformity in a multi-channel infrared detector, and the original infrared image is corrected by using the reference standard radiation source; the second type is to automatically calculate the correction coefficient according to the detected scene and automatically update the corresponding correction coefficient, for example, the chinese patent document with publication No. CN106342194a discloses a method for correcting the non-uniformity of an infrared image in a ground scene, and the processing process is performed synchronously with the input period of the image data, so as to implement iterative update of the correction coefficient.
The core idea of the first type of method is to measure the radiation response value of each probe element of the infrared focal plane detector to a fixed temperature by using a uniform radiation object as a radiation source, and obtain a non-uniform correction coefficient by a two-point method or a multi-point method. However, as the service time of the detector increases, the correction coefficient also changes, so that the calibration operation needs to be periodically performed, which affects the normal operation of the infrared system. In addition, the response of the detector is not linear, and the scaling method is not applicable to areas where the nonlinear response is severe. The second type of method can obtain corresponding calibration coefficients in real time during the working of the instrument, and complete real-time correction without affecting the normal working of the instrument, however, most of the currently proposed scene-based correction methods aim at the non-uniformity of the probe elements or the non-uniformity of the readout circuits independently, and lack a method capable of correcting two non-uniform noises simultaneously without losing image details.
Disclosure of Invention
The invention provides a method and a device for correcting the non-uniformity of an infrared image based on statistical characteristics, which can effectively overcome the influence of the response nonlinearity of a detector, achieve the effect of correcting the non-uniformity of two different types in the infrared image at the same time under the condition of not losing image details, can adapt to the non-uniformity correction of a large number of infrared images of various complex ground scenes, and effectively improve the imaging quality and definition of the images.
An infrared image non-uniformity correction method based on statistical characteristics comprises the following steps:
(1) Obtaining an S-shaped response curve of the detector according to the relation between the response DN value of the detector and the irradiation input;
(2) Arranging the obtained infrared images in random time into a three-dimensional data cube with O as an origin according to the positions of the probe elements; the yoz plane is an image space dimension, and the x-axis direction is an image frame dimension direction;
(3) Extracting an xoy plane of the cube image, wherein each row of data in the xoy plane has the same gain and bias coefficient; sorting the pixel values of each row of the extracted xoy plane from small to large, and dividing the xoy plane into M uniform areas according to the sorting of the pixel values, wherein the size of each block is L multiplied by C; l is the pixel column number of each block, and C is the pixel row number of each block;
(4) Calculating the average DN value of each uniform area and the average value of each row of each uniform area, wherein C is C;
(5) Segmenting an S-shaped response curve of the detector by the average DN value of each uniform region, and if the difference of the average DN values between the adjacent uniform regions of the continuous multiple blocks is within a preset range, considering that the uniform regions are on the same linear model;
(6) Dividing an S-shaped response curve of the detector into an H-section linear model by utilizing the step (5); selecting a dark uniform region and a bright uniform region in each section of linear model according to the contained uniform region quantity q, and solving the gain and bias coefficient of each section of linear model;
(7) For the infrared image to be corrected, searching which section of linear model of the pixel point is in the S-shaped response curve according to the gray value of the pixel point, and correcting each pixel point according to the gain and the bias coefficient of the corresponding section, thereby obtaining the corrected infrared image.
The method is based on rich pixel distribution brought by infrared images of a multi-feature scene, firstly, pixel points of all images acquired in a short time are counted, and the pixels are reordered according to the size of pixel values, so that uniformly-changed recombined data are obtained. And then extracting linear correlation areas in the recombined data by means of a decision algorithm, and finally simulating a response curve of the detector by utilizing the linear correlation of multiple areas, so that the non-uniform correction coefficient of each pixel point is calculated.
Further, in the step (3), after sorting the pixel values of each row of the extracted xoy plane from small to large, the minimum 10 columns of pixel values and the maximum 10 columns of pixel values are removed respectively.
In the step (4), the average DN value of each uniform area is calculated as follows:
wherein T is m Mean DN values representing M-th uniform regions, m=1, 2, 3..m, M representing the total number of uniform regions, DN (i, j) representing DN values of the ith column and jth row in each uniform region.
The average value of each row of each uniform area is expressed as:
in which Q m (c) Represents the average value of the c-th row of the m-th uniform region, and DN (i, c) represents the DN value of the c-th row of the i-th column.
In step (5), the difference between the average DN values of the consecutive blocks of adjacent uniform regions is within a preset range, which means that the difference between the average DN values is within the range of [0,10 ].
In the step (6), a rule of selecting a dark uniform region and a bright uniform region is as follows:
where a, b is a constant, and a dark uniform region and a bright uniform region are determined according to the size of q.
And solving gain and bias coefficients of each section of linear model, wherein the formula is as follows:
where k (i, j) represents the gain factor at the ith column, jth row; b (i, j) represents the bias factor at the ith column, jth row; q (Q) Dark and dark (j)、Q Bright (j) DN average values on j rows of dark uniform areas and bright uniform areas selected under the linear model; t (T) Dark and dark 、T Bright The DN mean value of the dark uniform area and the bright uniform area selected under the linear model.
An infrared image non-uniformity correction device based on statistical characteristics comprises a memory and one or more processors, wherein executable codes are stored in the memory, and the one or more processors are used for realizing the infrared image non-uniformity correction method when executing the executable codes.
Compared with the prior art, the invention has the following beneficial effects:
the method mainly utilizes the characteristic that response output of the same probe element to the same radiation energy input is consistent in a short time, firstly, a plurality of frames of infrared images acquired in a period of time are counted, the images are reordered into a uniform area according to the pixel value, then a multi-section linear model is constructed by the uniform area to approximate a nonlinear response curve of the detector, and finally, the non-uniform noise coefficients of different probe elements are obtained by a multi-section two-point method. The method utilizes the combination of multiple linear models to approximate the response curve of the detector, and can accurately approximate the curve by increasing the number of the linear models under the condition that the response of the detector is more nonlinear. Therefore, the method can simultaneously realize the correction of the non-uniformity of the probe element and the non-uniformity of the reading circuit without losing the details of the image, has simple algorithm and small calculated amount, can greatly simplify the complexity and cost of correction, and has good engineering implementation property.
Drawings
FIG. 1 is a schematic diagram of an S-type response curve of an infrared detector;
FIG. 2 is a flow chart of an infrared image non-uniformity correction method based on statistical characteristics according to the present invention;
FIG. 3 is a schematic diagram of the correction effect according to an embodiment of the present invention.
Detailed Description
The invention will be described in further detail with reference to the drawings and examples, it being noted that the examples described below are intended to facilitate the understanding of the invention and are not intended to limit the invention in any way.
First, the experimental setup of the present invention will be described. Computer configuration: 256GB host, inter (IR) Xeon (R) Gold 5218CPU@2.3GHz and NVIDIA GeForce RTX 3090GPU.
Next, a description will be given of a data set used in the present invention. In the invention, 80 clean infrared images are used, 800 non-uniform noise images are simulated through cutting and noise adding, and the original clean infrared images are used as the reference for qualitative and quantitative evaluation. In order to verify the effectiveness of the present invention, infrared images actually acquired by 600 detectors are also used as an actual reference. However, the invention has universality on the denoising effect of the infrared images acquired by different detectors in a short time, so that the invention is not influenced by the data set.
The basic principle of the invention is as follows: the response of each pixel of the infrared detector, each readout circuit, is nonlinear over the effective input range, approximating an 'S' curve, as shown in fig. 1. The abscissa is the incident irradiance and the ordinate is the gray value (DN value). Details of different probe elements, S-curvesThe shapes are different. For the S-shaped response curve of the detector, the invention approximates the S-shaped response curve to a combination of multiple linear models according to the radian of the response curve, and more linear models can be utilized for simulation even in the area with serious nonlinearity at the low end and the high end of the response of the detector. Therefore, the invention can fit different types of response curves and has the characteristic of universality. Responsive to DN values and irradiance inputSatisfies a multi-segment linear relationship, given by:
wherein,radiation input, DN, for the probe element input at the nth segment (i, j) n (i, j) is the response output of the probe element at the nth segment (i, j), a n (i, j) is the gain coefficient of the probe element at the nth segment (i, j), b n (i, j) is the bias coefficient of the probe element at the nth segment (i, j). The gain and bias coefficients for each pixel are calculated from two pieces of data consisting of relatively dark and bright pixel values for each piece. Wherein n: n is E N + (N + Positive integer) is the number of linear model segments approximated by the nonlinear response curve of the detector, and the detector is different in n value. i: i epsilon N + For the number of detector area arrays, j: j E N + Is the number of rows of the detector array.
As shown in fig. 2, the method for correcting the non-uniformity of the infrared image based on the statistical characteristics specifically includes the following steps:
s01, first, the acquired infrared images in a short time are arranged in a three-dimensional data cube (x, y, z) with O as an origin according to the positions of the probe elements. Wherein yoz plane is image space dimension, x-axis direction is image frame dimension direction, and frame number is F: f epsilon N + (N + Is a positive integer).
S02, extracting an xoy plane of the cube image, wherein each row of data in the xoy plane has the same gain and bias coefficient. For the extracted xoy plane, each row of pixel values is ordered from small to large, to reduce the effects of some individual DNs (e.g., saturation response and bad pixels), the smallest and largest 10 columns of pixels are discarded, leaving the remaining DNs as valid data.
S03, dividing the xoy plane into N uniform areas according to the gray value change in the step S02, wherein the size of each block is L multiplied by C (L epsilon N + ，C∈N + ). The average DN value of the m-th uniform region is denoted as T m ，m＝1，2，3...M：
Wherein the average value Q of the c-th row of the m-th block uniform region m (c) C=1, 2, 3..c, m=1, 2, 3..m, expressed as:
s04, after the block mean value and the row mean value of the recombined data are calculated in the step S03, the model is segmented through the block mean value. If the difference D_V of the mean values between adjacent uniform regions of successive blocks m-1 Substantially equal, consider the several regions to be on the same linear model, D_V m-1 The expression is as follows:
D_V m-1 ＝T m -T m-1
with the gray value range of the image being [0, 4095 ]]For example, D_V m-1 And D_V m-2 The fluctuation between [0,10]Within (1), consider D_V m-1 And D_V m-2 On the same segment of the linear model.
S05, the S-shaped response curve is assumed to be divided into an H-segment linear model. Within a segment of the linear model, q: m is E N + The block uniformity region can be used for solving the gain and bias coefficient of the linear model by selecting a dark uniformity region and a bright uniformity region, wherein the selection rule is as follows, a and b are constants, and the gain and bias coefficient are determined according to the q:
NUC coefficients (i.e., gain and bias coefficients) for pixels under each segment of the linear model can then be calculated from the following equation:
where i=1, 2, 3..h, j=1, 2, 3..c, H is the number of segments of the linear model that the S-type response curve approximates, C represents the number of lines of image pixels; k (i, j) represents the gain factor of the infrared array at (i, j); b (i, j) represents the bias factor of the infrared array at (i, j); q (Q) Dark and dark (j)、Q Bright (j) DN average values on j rows of dark uniform areas and bright uniform areas selected under the linear model; t (T) Dark and dark 、T Bright The DN mean value of the dark uniform area and the bright uniform area selected under the linear model.
S06, finally searching a section of linear model (uniform region) of the pixel point on the S curve according to the gray value of the pixel point, wherein the range of the section of linear model is the average value T of the brightest uniform region of the model n1 And darkest uniform region mean T n2 And correcting each pixel point according to the found NUC coefficient (namely the gain and the bias coefficient) of the corresponding segment.
Through the steps, the denoised infrared image can be obtained, and the correction results of the simulation noise image and the detector acquisition noise image are shown in fig. 3.
Based on the same inventive principle, the embodiment of the invention also provides an infrared image non-uniformity correction device based on statistical characteristics, which comprises a memory and one or more processors, wherein executable codes are stored in the memory, and the one or more processors are used for realizing the infrared image non-uniformity correction method when executing the executable codes.
The foregoing embodiments have described in detail the technical solution and the advantages of the present invention, it should be understood that the foregoing embodiments are merely illustrative of the present invention and are not intended to limit the invention, and any modifications, additions and equivalents made within the scope of the principles of the present invention should be included in the scope of the invention.
Claims (8)
1. An infrared image non-uniformity correction method based on statistical characteristics is characterized by comprising the following steps:
(1) Obtaining an S-shaped response curve of the detector according to the relation between the response DN value of the detector and the irradiation input;
(2) Arranging the obtained infrared images in random time into a three-dimensional data cube with O as an origin according to the positions of the probe elements; the yoz plane is an image space dimension, and the x-axis direction is an image frame dimension direction;
(3) Extracting an xoy plane of the cube image, wherein each row of data in the xoy plane has the same gain and bias coefficient; sorting the pixel values of each row of the extracted xoy plane from small to large, and dividing the xoy plane into M uniform areas according to the sorting of the pixel values, wherein the size of each block is L multiplied by C; l is the pixel column number of each block, and C is the pixel row number of each block;
(4) Calculating the average DN value of each uniform area and the average value of each row of each uniform area, wherein C is C;
(5) Segmenting an S-shaped response curve of the detector by the average DN value of each uniform region, and if the difference of the average DN values between the adjacent uniform regions of the continuous multiple blocks is within a preset range, considering that the uniform regions are on the same linear model;
(6) Dividing an S-shaped response curve of the detector into an H-section linear model by utilizing the step (5); selecting a dark uniform region and a bright uniform region in each section of linear model according to the contained uniform region quantity q, and solving the gain and bias coefficient of each section of linear model;
(7) For the infrared image to be corrected, searching which section of linear model of the pixel point is in the S-shaped response curve according to the gray value of the pixel point, and correcting each pixel point according to the gain and the bias coefficient of the corresponding section, thereby obtaining the corrected infrared image.
2. The method for correcting non-uniformity of an infrared image based on statistical characteristics according to claim 1, wherein in the step (3), after sorting the pixel values of each row of the extracted xoy plane from small to large, the minimum 10 columns of pixel values and the maximum 10 columns of pixel values are removed, respectively.
3. The method for correcting non-uniformity of an infrared image based on statistical properties according to claim 1, wherein in step (4), an average DN value of each uniform area is calculated as follows:
wherein T is m Represents the average DN value of the M-th uniform region, m=1, 2,3 … M, M represents the total number of uniform regions, and ND (i, j) represents the DN value of the i-th column and j-th row in each uniform region.
4. The method of correcting for non-uniformity of an infrared image based on statistical properties as set forth in claim 3, wherein in step (4), the average value of each line of each uniform region is given by:
in which Q m (c) Represents the average value of the c-th row of the m-th uniform region, and DN (i, c) represents the DN value of the c-th row of the i-th column.
5. The method of claim 1, wherein in the step (5), the difference between average DN values of adjacent uniform regions of consecutive blocks is within a predetermined range, which means that the difference between average DN values is within a range of 0, 10.
6. The method for correcting non-uniformity of an infrared image based on statistical properties according to claim 1, wherein in step (6), a rule of selecting a dark uniform region and a bright uniform region is as follows:
where a, b is a constant, and a dark uniform region and a bright uniform region are determined according to the size of q.
7. The method for correcting non-uniformity of an infrared image based on statistical characteristics according to claim 1, wherein in step (6), the gain and bias coefficients of each segment of linear model are solved according to the following formula:
where k (i, j) represents the gain factor at the ith column, jth row; b (i, j) represents the bias factor at the ith column, jth row; q (Q) Dark and dark (j)、Q Bright (j) DN average values on j rows of dark uniform areas and bright uniform areas selected under the linear model; t (T) Dark and dark 、T Bright The DN mean value of the dark uniform area and the bright uniform area selected under the linear model.
8. An infrared image non-uniformity correction apparatus based on statistical properties, comprising a memory and one or more processors, the memory having executable code stored therein, the one or more processors, when executing the executable code, being configured to implement the infrared image non-uniformity correction method of any one of claims 1-7.
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