CN109813442B - Multi-frame processing-based internal stray radiation non-uniformity correction method - Google Patents
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Abstract
The invention discloses a multi-frame processing-based internal stray radiation non-uniformity correction method, and belongs to the technical field of infrared imaging. The method adopts a one-point non-uniformity correction model related to an incident radiation value, takes a spatial gradient high-pass filtering result as a correction reference source, and calculates the gradient value of a correction bias value of each frame of image frame by using a minimum mean square error algorithm; in order to obtain a correction offset value of each frame of image, a polynomial model is used for establishing an offset value model, and parameters in the polynomial model are fitted according to gradient values of the correction offset value; repeating the above process to iteratively solve the correction offset value, and taking the current correction offset value as a point correction offset parameter of the next frame image; and (4) correcting each frame of image, and gradually improving the quality of the infrared imaging output after the nonuniformity correction along with the time progression. The method can correct the low-frequency noise of the airspace in the infrared imaging system, and has the advantages of more thorough removal of low-frequency non-uniform noise and stronger robustness.
Description
Technical Field
The invention relates to a multiframe processing-based internal stray radiation non-uniformity correction method, and belongs to the technical field of infrared imaging.
Background
Fixed pattern noise caused by the non-uniformity of the infrared imaging device is a key factor influencing the imaging quality of the infrared imaging device, and a non-uniformity Correction (non-uniformity Correction) algorithm needs to be introduced into subsequent image processing to eliminate the noise. The heterogeneity of the infrared focal plane array (IRFPA) imaging system mainly comprises (1) the non-uniform responsivity of each unit of the detector due to the limitation of the manufacturing process and the reading circuit; (2) the superposition on the scene radiation leads to imaging inhomogeneities, for example the well-known daffodil effect, due to stray radiation from the inclusion of optical systems and mechanical structures inside the imaging system. The former often appears as spatial high-frequency noise on the image, such as dots, stripes; in contrast, the latter superimposed radiation appears as spatially continuous low-frequency noise on the image, since it is not in the image plane, and these non-uniform noises drift slowly with increasing operating time and temperature.
At present, the commonly used Calibration and correction (CBNUC) Based on a radiation reference source and adaptive correction (SBNUC) Based on a Scene have a good effect on removing high-frequency Non-uniform noise in an airspace, but have a poor effect on low-frequency noise in an image airspace caused by stray radiation inside a system. Therefore, a common method for removing such noise is to design the front-end optical system and the internal structure of the thermal imager. The existing algorithms for compensating the internal stray radiation of the infrared imaging system by using digital image processing are few, and a single-frame processing correction algorithm does not utilize the time-domain slowly-varying characteristic of the internal stray radiation noise and has limited correction effect; correction algorithms for multi-frame statistics inevitably produce "ghosting" due to overcorrection.
Disclosure of Invention
The invention discloses a multi-frame processing-based internal stray radiation non-uniformity correction method, which aims to: the method corrects the low-frequency noise of the airspace caused by the internal stray radiation in the infrared imaging system, and has the advantages of more thorough removal of low-frequency non-uniform noise and stronger robustness.
The invention discloses an internal stray radiation non-uniformity correction method based on multi-frame processing, which adopts a one-point non-uniformity correction model related to an incident radiation value, takes a spatial gradient high-pass filtering result as a correction reference source, and calculates the gradient value of a correction bias value of each frame of image frame by using a Least Mean Square (LMS) algorithm. In order to obtain the correction offset value of each frame of image, a polynomial model is used for establishing an offset value model, and parameters in the polynomial model are fitted according to the gradient value of the correction offset value. And repeating the above process to iteratively solve the correction offset value, and taking the current correction offset value as a one-point correction offset parameter of the next frame image. And (4) correcting each frame of image, and gradually improving the quality of the infrared imaging output after the nonuniformity correction along with the time progression.
The invention discloses an internal stray radiation non-uniformity correction method based on multi-frame processing, which comprises the following steps:
step 1, correcting an original input image by using a one-point non-uniform correction model according to a correction bias value obtained by processing a previous frame of image, and removing airspace low-frequency non-uniform noise in the image caused by internal stray radiation to obtain a high-quality image from which the airspace low-frequency non-uniform noise is removed. For the first frame image, the correction offset value initial value is set to 0.
The one-point correction model in the step 1 is as the formula (1)
Zn(i,j)＝yn(i,j)+bn(i,j) (1)
Wherein Z isn(i, j) represents a gray value after one-point correction; bn(i, j) is a bias correction parameter for the pixel element (i, j) to compensate for internal radiation induced non-uniformity.
And 2, performing spatial gradient filtering on the image through a spatial gradient high-pass filter to remove the low-frequency non-uniform noise gradient in the image.
The spatial gradient high-pass filter in the step 2 is shown as a formula (2),
wherein the content of the first and second substances,
representing gradient operator, | X! Yw×wRepresenting the mean of X in a w window centered on i, j, and ST is the noise similarity. The function f is used to estimate the ideal image without low frequency noise, i.e. to reject low gradient components caused by internal radiation in the image. The gradient of the internal radiation is continuously variable and small, and the ST value is set according to a trade-off between the degree of noise rejection of the filtered image and the detail retention capability.
The spatial gradient high-pass filter in the step 2 has the following functions: regardless of the direction of the gradient, the gradient within the window where the mean is greater than the threshold is more likely to be caused by the scene and is preserved, the gradient where the mean is less than the threshold, the closer to the threshold the smaller the magnitude of the reduction, and conversely the greater the magnitude of the reduction.
According to engineering experience, the ST value in the step 2 is selected to be 4-10, and is further preferably set to be 4.
Step 3, filtering the result through a spatial gradient
As a correction approximation value, an error function E is constructed, and the error function E is minimized by a gradient descent method to obtain a gradient of a correction offset value
Filtering the result by spatial gradient
As a correction approximation, an error function E is constructed as in equation (3)
Wherein, yn(i, j) represents the gray value of the picture element (i, j) before correction, bn(i, j) represents a bias correction parameter for the picture element (i, j) to compensate for internal radiation induced non-uniformity.
Minimizing the error function E by gradient descent to obtain a gradient of corrected bias value
Wherein epsilonnAnd (i, j) represents an iterative learning factor and determines the performance of a Least Mean Square (LMS) algorithm. Learning factor epsilonnThe larger (i, j), the faster the learning rate, but the smaller the learning factor εn(i, j) can ensure the stability of the algorithm. Learning factor epsilon in neural network-based non-uniformity correction algorithmsnThe (i, j) value should become large when the correction approximation value estimation is accurate and small when the estimation is not accurate. According to the principle that the filter in the formula (2) eliminates the internal radiation noise, the internal radiation is notThe uniformity noise is obvious in a uniform area in the image, and the gradient of the uniformity noise is slightly influenced by a scene, so that the filtering estimation result is more accurate. In contrast, in the area with rich scene details, the removed low-frequency information has gradients in the scene, and the estimation error of the approximated gradient value is large. Thus, the local standard deviation of the image is used to control the learning factor εn(i, j), as follows:
wherein σZ(i,j)Representing the spatial local standard deviation of the image, and d is the maximum learning factor. As shown in the formula (5), in a smooth region with a small local standard deviation of the image, the estimation of the correction approximate value of the image gradient is more accurate, so that the learning factor epsilonn(i, j) greater; when the local standard deviation is large, the learning factor epsilonn(i, j) is smaller.
In addition, in order to prevent f estimation error accumulation when the scene is static, which causes the internal radiation gradient estimation to continuously deviate from the true value, and leads the image to introduce false low-frequency information, the time domain threshold G is set through the formula (6), and the learning factor epsilon is subjected tonAnd (i, j) regulating and controlling to ensure that the gradient of the pixel correction parameter of the static area is not updated.
Where G is the time domain threshold, Ln(i, j) is used to detect temporal changes in gray levels, as shown in equation (7),
and 4, solving the correction offset value by using the gradient value of the correction offset value obtained in the step 3. And establishing an offset value gray model by using the polynomial model, and fitting parameters in the polynomial model according to the gradient value of the corrected offset value, namely solving the corrected offset value to be used as a one-point corrected offset parameter of the next frame of image.
And (3) establishing an offset value gray model by using the polynomial model, and fitting parameters in the polynomial model according to the gradient value for correcting the offset value. And (4) obtaining the gradient of the correction parameter of each frame through the frame-by-frame iteration of the formula (4). To find the correction parameters for each frame, more known quantities are needed in addition to correcting for the bias parameter gradients. When the correction offset value of the image is expressed by a polynomial, the internal radiation correction offset value of the infrared focal plane array imaging system is expressed as
Wherein m and gamma are vector form, m is row vector, each column is polynomial formed by i and j in formula (8), gamma is column vector, and each row is parameter corresponding to each term in the polynomial. N represents the order of the polynomial. The larger N is, the higher spatial frequency can exist in the image g, and conversely, only spatial low-frequency components exist in the image g. For low-frequency noise in the airspace, N is selected<The value of 10 ensures the stability of the correction results. To this end, only the gradient of g needs to be fitted to
Obtaining gamma, and establishing an error function E (gamma) as shown in the formula (9)
Where I denotes the coordinates (I, j), P denotes the total number of pixels of the image, and g and b are in the form of vectors. Solving the least squares optimization problem of the error function E (γ) as in equation (9), deriving the error function E with respect to γ, making the derivative equal to zero to obtain a closed solution:
γ＝(MTM)-1MTB (10)
wherein M is a matrix of 2P × (N +1) (N +2)/2-1, (M)TM)-1MTCalculated in advance and stored as a look-up table.
And 5: and (4) repeating the steps 1 to 4, correcting each frame of image, gradually improving the quality of the infrared imaging output after the non-uniformity correction along with the time progression until an image meeting the infrared imaging quality requirement is obtained, and terminating the iteration to obtain the corresponding high-quality infrared imaging output after the non-uniformity correction.
Has the advantages that:
compared with a correction method based on a single-frame image, the internal stray radiation non-uniformity correction method based on multi-frame processing disclosed by the invention removes non-uniformity noise frame by frame from a time domain by utilizing the characteristic of a fixed position of the non-uniformity noise in the multi-frame image, the single-frame image has smaller influence on the correction effect, and the low-frequency non-uniformity noise is removed more thoroughly, so that the robustness is stronger.
Drawings
FIG. 1 is a flow chart of the multi-frame processing based internal spurious radiation non-uniformity correction algorithm of the present invention.
FIG. 2 is a data image during the correction process of the multi-frame processing-based internal stray radiation non-uniformity correction algorithm of the present invention, wherein: (a) for estimating the value of incident radiation
Is an estimate of the incident radiation
Is the gradient of the correction bias value, and (c) is the gradient of the correction bias value
A longitudinal component of (d) a gradient of the corrected bias value
Is a corrected offset estimate bn(i,j)。
FIG. 3 is a graph comparing the correction effect of the present invention and Sf method on the low frequency FPN of internal radiation (low frequency FPN of internal radiation of North Guang Wei uncooled long wave infrared focal plane thermal imager), wherein: (a) the image with noise, (b) the correction effect of the Sf method, and (c) the correction effect of the invention.
Fig. 4 is a comparison graph of the correction effect of the cold reflection effect (128 × 128 refrigerated thermal imager cold reflection effect) using the present invention and Sf method, respectively, in which: (a) the image with noise, (b) the correction effect of the Sf method, and (c) the correction effect of the invention.
Detailed description of the invention
The following further describes the embodiments of the present invention with reference to the drawings.
In the embodiment, a North Guang Wei uncooled long-wave infrared focal plane thermal imager (VOx detector scale 384 multiplied by 288, pixel spacing 25 mu m, 14bit output and frame frequency 50Hz) is adopted to collect a video sequence to test the correction effect of the inner radiation low-frequency FPN. In order to obtain a real image with internal radiation noise, after startup calibration correction, the baffle is turned off for correction, and the thermal imager continuously works for 2 hours at the summer ambient temperature of 36-38 ℃. At this point the internal temperature of the thermal imager has risen and low frequency noise similar to corner heat appears in the viewed image from the display. And starting to collect a video sequence by taking an outdoor complex scene as a target.
Fig. 1 shows a flow chart of the method in this embodiment, and the method for correcting nonuniformity of internal stray radiation based on multi-frame processing disclosed in this embodiment includes the following specific steps:
step 1, carrying out spatial filtering on the image by using a formula (2) to obtain an incident radiation estimation value
For the 432 th frame of the video sequence (as shown in fig. 3 (a)), the incident radiation estimation value is obtained when calculating the correction parameter of the 432 th frame of the video sequence
Using 431 th frame image, utilizing formula (2) to make spatial filtering so as to obtain incident radiation estimated value
The component in the longitudinal direction is shown in fig. 2(a), and the gradient in the transverse direction is shown in fig. 2 (b).
Step 2, calculating the correction bias estimated value gradient
Obtaining the incident radiation estimated value of the 431 th frame image
Then, an error function is constructed by using the formula (3), the error function is minimized by a gradient descent method, and the gradient of the correction offset value of the 432 th frame image is obtained
The component in the longitudinal direction is shown in fig. 2(c), and the gradient in the transverse direction is shown in fig. 2 (d).
Step 3, calculating a correction offset estimated value b432(i, j). According to the formulas (8) to (11), the correction bias prediction value b of the 432 th frame image is calculated432(i, j) as shown in FIG. 2 (e).
And 4, performing one-point correction on the 432 th frame image according to the formula (1) to obtain a correction result of the 432 th frame image in the step (c) of fig. 3.
In the course of correcting the images of the 1042 th and 1254 th frames
b1042(i,j)、
b1254(i, j) as shown in fig. 2, the image correction results of the 1042 th frame and the 1254 th frame are also obtained.
Fig. 3 shows some typical scenes (images before and after correction of frame 432, frame 1042, and frame 1254) after processing a video sequence, and the flow of correcting frame 432 is shown as the above specific implementation steps, and the same steps are used to obtain the correction results of the image of frame 1042 and frame 1254. As seen from fig. 3, after the Sf method is used for correction, light spots become weak, the image quality becomes good, but an obvious dark spot still exists in the middle of an image, and low-frequency radiation noise exists at four corners; the invention basically removes low-frequency noise caused by internal radiation, and the visual effect of the image is better.
The above detailed description is intended to illustrate the objects, aspects and advantages of the present invention, and it should be understood that the above detailed description is only exemplary of the present invention and is not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A multi-frame processing-based internal stray radiation nonuniformity correction method is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
step 1, correcting an original input image by using a one-point non-uniform correction model according to a correction bias value obtained by processing a previous frame of image, and removing airspace low-frequency non-uniform noise in the image caused by internal stray radiation to obtain a high-quality image from which the airspace low-frequency non-uniform noise is removed; setting the initial value of the correction offset value to 0 for the first frame image;
step 2, performing spatial gradient filtering on the image through a spatial gradient high-pass filter to remove the low-frequency non-uniform noise gradient in the image;
step 3, filtering the result through a spatial gradient
As a correction approximation value, wherein z is the gray value of the original input image after one point correction in step 1;
constructing an error function E, and minimizing the error function E by a gradient descent method to obtain the gradient of the corrected offset value
(i, j) is pixel coordinates, n represents the frame number of the image, and n +1 represents the (n +1) th frame of the image;
step 4, solving the correction offset value by using the gradient value of the correction offset value obtained in the step 3; establishing an offset value gray model by using the polynomial model, and fitting parameters in the polynomial model according to the gradient value of the corrected offset value, namely solving the corrected offset value to be used as a one-point corrected offset parameter of the next frame of image;
and 5: and (4) repeating the steps 1 to 4, correcting each frame of image, gradually improving the quality of the infrared imaging output after the non-uniformity correction along with the time progression until an image meeting the infrared imaging quality requirement is obtained, and terminating the iteration to obtain the corresponding high-quality infrared imaging output after the non-uniformity correction.
2. The multi-frame processing-based internal spurious radiation non-uniformity correction method of claim 1, wherein: the one-point correction model in the step 1 is as the formula (1)
Zn(i,j)＝yn(i,j)+bn(i,j) (1)
Wherein Z isn(i, j) represents a gray value after one-point correction; bn(i, j) is a bias correction parameter for the pixel element (i, j) to compensate for internal radiation induced non-uniformity.
3. The multi-frame processing-based internal spurious radiation non-uniformity correction method of claim 2, wherein: the spatial gradient high-pass filter in the step 2 is shown as a formula (2),
wherein the content of the first and second substances,
representing gradient operator, | X! Yw×wRepresenting the mean value of X in a w multiplied by w window by taking i and j as the center, and ST is the noise similarity; the function f is used for estimating an ideal image without low-frequency noise, namely, low-gradient components caused by internal radiation in the image are removed; the gradient of the internal radiation being continuously variable and small, according to filteringThe ST value is set by a tradeoff of the degree of noise rejection of the wave image and the detail retention capability.
4. A multiframe-processing-based internal spurious radiation non-uniformity correction method as claimed in claim 3, characterized in that: the specific implementation method of the step 3 is that,
filtering the result by spatial gradient
As a correction approximation, an error function E is constructed as in equation (3)
Wherein, yn(i, j) represents the gray value of the picture element (i, j) before correction, bn(i, j) represents a bias correction parameter for the pixel element (i, j) to compensate for internal radiation induced non-uniformity;
minimizing the error function E by gradient descent to obtain a gradient of corrected bias value
Wherein epsilonn(i, j) represents iterative learning factors and determines the performance of the minimum mean square error algorithm; learning factor epsilonnThe larger (i, j), the faster the learning rate, but the smaller the learning factor εn(i, j) the stability of the algorithm can be guaranteed; learning factor epsilon in neural network-based non-uniformity correction algorithmsnThe value (i, j) should become larger when the estimation of the correction approximation value is accurate and smaller when the estimation is not accurate; according to the principle that the filter in the formula (2) eliminates the internal radiation noise, the internal radiation non-uniformity noise is obvious in a uniform area in the image, and the gradient of the internal radiation non-uniformity noise is slightly influenced by a scene, so that the filtering estimation result is more accurate; on the contrary, in the scene detailIn a rich area, the removed low-frequency information has gradients in a scene, and the estimation error of the approximated gradient value is large; thus, the local standard deviation of the image is used to control the learning factor εn(i, j), as follows:
wherein σZ(i,j)Representing the spatial domain local standard deviation of the image, wherein d is the maximum learning factor; as shown in the formula (5), in a smooth region with a small local standard deviation of the image, the estimation of the correction approximate value of the image gradient is more accurate, so that the learning factor epsilonn(i, j) greater; when the local standard deviation is large, the learning factor epsilonn(i, j) smaller;
in addition, in order to prevent f estimation error accumulation when the scene is static, which causes the internal radiation gradient estimation to continuously deviate from the true value, and leads the image to introduce false low-frequency information, the time domain threshold G is set through the formula (6), and the learning factor epsilon is subjected ton(i, j) regulating and controlling to ensure that the gradient of the pixel correction parameter of the static area is not updated;
where G is the time domain threshold, Ln(i, j) is used to detect temporal changes in gray levels, as shown in equation (7),
5. the multi-frame processing-based internal spurious radiation non-uniformity correction method of claim 4, wherein: the specific implementation method of the step 4 is that,
establishing an offset value gray model by using the polynomial model, and fitting parameters in the polynomial model according to the gradient value for correcting the offset value; obtaining the gradient of each frame of correction parameters through the frame-by-frame iteration of a formula (4); in order to obtain the correction parameters of each frame, more known quantities are needed besides the correction of the gradient of the bias parameters; when the correction offset value of the image is expressed by a polynomial, the internal radiation correction offset value of the infrared focal plane array imaging system is expressed as
Wherein m and gamma are vector forms, m is a row vector, each column is a polynomial formed by i and j in formula (8), gamma is a column vector, each row is a parameter corresponding to each term in the polynomial, N represents the order of the polynomial, the larger N is, the higher spatial frequency which can exist in an image g is, on the contrary, only airspace low-frequency components exist in the image g, for airspace low-frequency noise, the value N <10 is selected to ensure the stability of a correction result, therefore, gamma is obtained only by fitting the gradient of g to ▽ b, and an error function E (gamma) is established as formula (9)
Wherein I represents the coordinates (I, j), P represents the total number of pixels of the image, and g and b are in the form of vectors; solving the least squares optimization problem of the error function E (γ) as in equation (9), deriving the error function E with respect to γ, making the derivative equal to zero to obtain a closed solution:
γ＝(MΤM)-1MΤB (10)
wherein M is a matrix of 2P × (N +1) (N +2)/2-1, (M)TM)-1MTCalculated in advance and stored as a look-up table;
6. a multiframe-processing-based internal spurious radiation non-uniformity correction method as claimed in claim 2, 3, 4 or 5, characterized in that: the spatial gradient high-pass filter in the step 2 has the effect that, regardless of the direction of the gradient, the gradient with the mean value larger than the threshold value in the window is more likely to be caused by the scene and is reserved, and the gradient with the mean value smaller than the threshold value is closer to the threshold value, the smaller the reduction amplitude is, and conversely, the larger the reduction amplitude is.
7. The multi-frame processing-based internal spurious radiation non-uniformity correction method of claim 6, wherein: and (4) selecting the ST value in the step 2 to be 4-10 according to engineering experience.
8. The multi-frame processing-based internal spurious radiation non-uniformity correction method of claim 7, wherein: and 2, selecting the ST value to be 4.
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