CN108225570B - Short wave infrared focal plane self-adaptive non-uniform correction algorithm - Google Patents
Short wave infrared focal plane self-adaptive non-uniform correction algorithm Download PDFInfo
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Abstract
The invention discloses a short-wave infrared focal plane self-adaptive non-uniform correction algorithm which uses a logistic function to combine with the non-uniformity of a short-wave infrared focal plane non-uniform model focusing plane to carry out regression estimation to obtain a non-uniform correction coefficient so as to achieve the purpose of correction.
Description
Technical Field
The invention relates to the technical field of short wave infrared focal planes, in particular to a short wave infrared focal plane self-adaptive non-uniform correction algorithm.
Background
The nonuniformity of the short-wave 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.
Therefore, it is desirable to have a short-wave infrared focal plane adaptive non-uniform correction algorithm that can effectively solve the above-mentioned drawbacks in the prior art.
Disclosure of Invention
The invention aims to provide a short-wave infrared focal plane self-adaptive non-uniformity correction algorithm based on logics and a multi-factor correction model, wherein the heterogeneity correction coefficient is obtained by performing regression estimation on the heterogeneity of a focusing plane by combining a logics function with the non-uniformity model of the short-wave infrared focal plane, so that the purpose of correction is achieved.
In order to achieve the purpose, the invention provides a short-wave infrared focal plane self-adaptive non-uniform correction algorithm based on logics and a multi-factor correction model, which comprises the following steps of:
① two preconditions are made for the linear model;
② ignoring the errors of the detection unit in small radiation range and saturation removal, the focal plane response is approximately considered linear, and under uniform irradiance conditions, the non-uniformity linear model of the focal plane is expressed by the following equation:
Yk(i,j)＝ak(i,j)*Xk(i,j)-bk(i,j)+nk(i,j)
in the formula, Xk(i, j) represents the true infrared radiance received by the detecting element with the coordinate of (i, j) at the k frame, ak(i, j) and bk(i, j) are respectively the multiplicative noise variation and the additive noise variation of the detection unit at this time, nk(i, j) is the electronic noise of the readout circuit, ak(i, j) and bkThe value of (i, j) also changes with time;
③, obtaining that the nonuniformity of the focal plane is mainly composed of multiplicative gain and additive bias through step ②, the bias mainly affecting the nonuniformity of the focal plane is the bias, and the bias is continuously changed along with the change of the environment temperature, the detector temperature and the working time, so many scene correction algorithms are appeared, only the continuous prediction compensation of the bias is performed, and a good effect is obtained under the condition that the scene is continuously moved;
④ the nonuniformity of the bias value is decomposed into the combination of environment temperature and detector temperature and several influence factors plus a fixed mean bias to form a model:
Yk(i,j)＝Ak(i,j)*Xk(i,j)+Bk(i,j)*TFPA+Ck(i,j)*TENV+m
in the formula, Ak(i, j) and Bk(i,j)、Ck(i, j) are respectively a focal plane non-uniformity response coefficient, a detector temperature non-uniformity coefficient and a working time non-uniformity coefficient, m is a fixed mean value bias and is ignored in the correction;
⑤ assuming that the infrared focal plane image has continuous inter-pel grey levels, the difference between adjacent pels in the infrared image is statistically small for a certain number of frames, which means that two adjacent pels are almost equal in the histogram of time;
⑥ approximate the true response value of an individual probe unit to the mean response value Y of the neighborhood probes, based on the assumptions of step ⑤, as an estimate of the logistic regression:
then the logistic function model of the non-uniformity correction estimate is
⑦ according to the above non-uniformity correction model, the logistic function model of non-uniformity correction is:
⑧ the parameters of the non-uniformity correction are found using maximum likelihood estimation as follows:
solving for Ak(i, j) and Bk(i,j)、Ck(i, j) the focal plane non-uniformity response coefficient, the detector temperature non-uniformity coefficient and the working time non-uniformity coefficient, and specifically solving by using a Newton-Raphson iteration method.
Preferably, the two preconditions for the linear model include: (1) the response of each detection unit in the focal plane is stable in time and cannot be changed due to the length of the working time; (2) the working area of each detection unit is linear.
Aiming at the characteristic of nonuniformity of a short wave infrared focal plane and the defect of shutter correction, the invention provides a short wave infrared focal plane self-adaptive nonuniform correction algorithm based on logics and a multi-factor correction model, which comprehensively considers influence factors of various nonuniformity of environment temperature, detector response and detector temperature, thereby well solving the uncertainty of nonuniformity correction of the short wave focal plane, and quantitative test results show that the method can reach 0.001% of nonuniformity correction residues, remarkably improve the correction precision, improve the image quality of infrared imaging and expand the application range of the correction technology.
Detailed Description
The short wave infrared focal plane self-adaptive non-uniform correction algorithm based on the logics and the multi-factor correction model comprises the following steps of:
① the two premise assumptions of the traditional linear model are that the response of each detecting unit in the focal plane is stable in time and will not change due to the length of the working time;
② ignoring the errors of the detection unit in small radiation range and saturation removal, the focal plane response is approximately considered linear, and under uniform irradiance conditions, the non-uniformity linear model of the focal plane is expressed by the following equation:
Yk(i,j)＝ak(i,j)*Xk(i,j)-bk(i,j)+nk(i,j)
in the formula, Xk(i, j) represents the true infrared radiance received by the detecting element with the coordinate of (i, j) at the k frame, ak(i, j) and bk(i, j) are respectively the multiplicative noise variation and the additive noise variation of the detection unit at this time, nk(i, j) is the electronic noise of the readout circuit, ak(i, j) and bkThe value of (i, j) also changes with time;
③, obtaining that the nonuniformity of the focal plane is mainly composed of multiplicative gain and additive bias through step ②, the bias mainly affecting the nonuniformity of the focal plane is the bias, and the bias is continuously changed along with the change of the environment temperature, the detector temperature and the working time, so many scene correction algorithms are appeared, only the continuous prediction compensation of the bias is performed, and a good effect is obtained under the condition that the scene is continuously moved;
④ the nonuniformity of the bias value is decomposed into the combination of environment temperature and detector temperature and several influence factors plus a fixed mean bias to form a model:
Yk(i,j)＝Ak(i,j)*Xk(i,j)+Bk(i,j)*TFPA+Ck(i,j)*TENV+m
in the formula, Ak(i, j) and Bk(i,j)、Ck(i, j) are respectively a focal plane non-uniformity response coefficient, a detector temperature non-uniformity coefficient and a working time non-uniformity coefficient, m is a fixed mean value bias and is ignored in the correction;
⑤ assuming that the infrared focal plane image has continuous inter-pel grey levels, the difference between adjacent pels in the infrared image is statistically small for a certain number of frames, which means that two adjacent pels are almost equal in the histogram of time;
⑥ approximate the true response value of an individual probe unit to the mean response value Y of the neighborhood probes, based on the assumptions of step ⑤, as an estimate of the logistic regression:
then the logistic function model of the non-uniformity correction estimate is
⑦ according to the above non-uniformity correction model, the logistic function model of non-uniformity correction is:
⑧ the parameters of the non-uniformity correction are found using maximum likelihood estimation as follows:
solving for Ak(i, j) and Bk(i,j)、Ck(i, j) the focal plane non-uniformity response coefficient, the detector temperature non-uniformity coefficient and the working time non-uniformity coefficient, and specifically solving by using a Newton-Raphson iteration method.
In practical application, the gradient ascent algorithm is used, and iteration is continued. The gradient-rise algorithm has a slow convergence rate compared to newton iterations, since the gradient-rise algorithm converges first-order, whereas newton iterations belong to second-order convergence.
The algorithm is practically applied, the corrected logics estimation window is a statistical window of 3 multiplied by 3, the correction coefficient is converged to be optimal after 100 frames of images generally by a gradient rising method, the corrected images are uniform, the correction effect is obvious, in addition, the algorithm adopts a multivariate correction model, when the scene is static or the motion is slow, excessive correction can not occur, so that ghost image is caused, the scene motion is not required, the calculation is simple, the real-time parallel processing is convenient, and the practical limitation of the self-adaptive non-uniformity correction is broken through
Finally, it should be pointed out that: the above examples are only for illustrating the technical solutions of the present invention, and are not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (2)
1. A short wave infrared focal plane self-adaptive non-uniform correction algorithm is characterized by comprising the following steps:
① two preconditions are made for the linear model;
② ignoring the errors of the detection unit in small radiation range and saturation removal, the focal plane response is approximately considered linear, and under uniform irradiance conditions, the non-uniformity linear model of the focal plane is expressed by the following equation:
Yk(i,j)＝ak(i,j)*Xk(i,j)-bk(i,j)+nk(i,j)
in the formula, Xk(i, j) represents the true infrared radiance received by the detecting element with the coordinate of (i, j) at the k frame, ak(i, j) and bk(i, j) are respectively the multiplicative noise variation and the additive noise variation of the detection unit at this time, nk(i, j) is the electronic noise of the readout circuit, ak(i, j) and bkThe value of (i, j) also changes with time;
③, obtaining that the nonuniformity of the focal plane is mainly composed of multiplicative gain and additive bias through step ②, the bias mainly affecting the nonuniformity of the focal plane is the bias, and the bias is continuously changed along with the change of the environment temperature, the detector temperature and the working time, so many scene correction algorithms are appeared, only the continuous prediction compensation of the bias is performed, and a good effect is obtained under the condition that the scene is continuously moved;
④ the nonuniformity of the bias value is decomposed into the combination of environment temperature and detector temperature and several influence factors plus a fixed mean bias to form a model:
Yk(i,j)＝Ak(i,j)*Xk(i,j)+Bk(i,j)*TFPA+Ck(i,j)*TENV+m
in the formula, Ak(i, j) and Bk(i,j)、Ck(i, j) are respectively a focal plane non-uniformity response coefficient, a detector temperature non-uniformity coefficient and a working time non-uniformity coefficient, m is a fixed mean value bias and is ignored in the correction;
⑤ assuming that the infrared focal plane image has continuous inter-pel grey levels, the difference between adjacent pels in the infrared image is statistically small for a certain number of frames, which means that two adjacent pels are almost equal in the histogram of time;
⑥ approximate the true response value of an individual probe unit to the mean response value Y of the neighborhood probes, based on the assumptions of step ⑤, as an estimate of the logistic regression:
then the logistic function model of the non-uniformity correction estimate is
⑦ according to the above non-uniformity correction model, the logistic function model of non-uniformity correction is:
⑧ the parameters of the non-uniformity correction are found using maximum likelihood estimation as follows:
solving for Ak(i, j) and Bk(i,j)、Ck(i, j) focal plane non-uniformity response coefficient, detector temperature non-uniformity coefficient and working time non-uniformity coefficient, and specifically solving the problem of Newton-Solving by a Lafersen iterative method.
2. The short wave infrared focal plane adaptive non-uniform correction algorithm of claim 1, characterized in that: two preconditions for the linear model include: (1) the response of each detection unit in the focal plane is stable in time and cannot be changed due to the length of the working time; (2) the working area of each detection unit is linear.
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| CN101776486A (en) * | 2009-12-31 | 2010-07-14 | 华中科技大学 | Method for correcting non-uniformity fingerprint pattern on basis of infrared focal plane |
| CN102855610A (en) * | 2012-08-03 | 2013-01-02 | 南京理工大学 | Method for correcting infrared image heterogeneity by using parameter correctness factor |
| KR101282567B1 (en) * | 2013-04-25 | 2013-07-04 | 주식회사 콕스 | Apparatus and method for amending non-uniformity of handheld thermal imaging camera |
| CN103604503A (en) * | 2013-06-17 | 2014-02-26 | 中国航天科工集团第三研究院第八三五八研究所 | High-precision non-uniformity correction method for dynamic adjustment of integration time |
| CN104268870A (en) * | 2014-09-24 | 2015-01-07 | 北京津同利华科技有限公司 | Short-wave infrared focal plane non-uniformity correction algorithm based on wavelet transformation histogram |
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| US6476392B1 (en) * | 2001-05-11 | 2002-11-05 | Irvine Sensors Corporation | Method and apparatus for temperature compensation of an uncooled focal plane array |
| KR100586308B1 (en) * | 2005-04-15 | 2006-06-08 | 한국과학기술원 | Compensation circuit which compensates non-uniformity according to change of operating temperature of bolometer |
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| CN101776486A (en) * | 2009-12-31 | 2010-07-14 | 华中科技大学 | Method for correcting non-uniformity fingerprint pattern on basis of infrared focal plane |
| CN102855610A (en) * | 2012-08-03 | 2013-01-02 | 南京理工大学 | Method for correcting infrared image heterogeneity by using parameter correctness factor |
| KR101282567B1 (en) * | 2013-04-25 | 2013-07-04 | 주식회사 콕스 | Apparatus and method for amending non-uniformity of handheld thermal imaging camera |
| CN103604503A (en) * | 2013-06-17 | 2014-02-26 | 中国航天科工集团第三研究院第八三五八研究所 | High-precision non-uniformity correction method for dynamic adjustment of integration time |
| CN104268870A (en) * | 2014-09-24 | 2015-01-07 | 北京津同利华科技有限公司 | Short-wave infrared focal plane non-uniformity correction algorithm based on wavelet transformation histogram |
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