Real-time non-uniformity correction of infrared imaging system

Geng-fei LI; Gui-ju LI; Guang-liang HAN; Pei-xun LIU; Shan JIANG

doi:10.3788/OPE.20162411.2841

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Real-time non-uniformity correction of infrared imaging system

Information science | 更新时间：2020-07-07

- Real-time non-uniformity correction of infrared imaging system
- Editorial Office of Optics and Precision Engineeri Vol. 24, Issue 11, Pages: 2841-2847(2016)
- 作者机构：
  
  中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162411.2841
  CLC： TN216
- Received：13 May 2016，
  
  Accepted：20 June 2016，
  
  Published：25 November 2016
- 稿件说明：
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Geng-fei LI, Gui-ju LI, Guang-liang HAN, et al. Real-time non-uniformity correction of infrared imaging system[J]. Editorial office of optics and precision engineeri, 2016, 24(11): 2841-2847.
DOI：

Geng-fei LI, Gui-ju LI, Guang-liang HAN, et al. Real-time non-uniformity correction of infrared imaging system[J]. Editorial office of optics and precision engineeri, 2016, 24(11): 2841-2847. DOI： 10.3788/OPE.20162411.2841.

摘要

针对红外相机系统响应非均匀性所产生的竖条纹固定噪声，提出了基于多线程优化的单帧红外图像非均匀性实时校正算法。该算法参考全变分理论的非均匀性校正算法确定噪声与图像输出之间的加性校正关系，建立了描述条纹噪声的数学模型；通过滤波算法将图像分为高频分量和低频分量，运用建立的条纹噪声模型，基于最小二乘法从高频部分中拟合出条纹噪声。最后，使用全变分理论确定的加性关系对图像进行校正。为了提高算法的计算速度，运用多线程技术对算法进行了优化。对提出的算法与MIRE（Midway Infrared Equalization）算法及传统双边滤波算法进行了图像质量评价和性能对比。结果显示：本文算法使图像非均匀性较原图降低了3%；算法效率与MIRE算法无异，系统运行时间在320×256的14位红外图像上为1.5 ms/frame，达到了工程标准。

Abstract

A real-time Non-Uniformity Correction(NUC) algorithm for a single-frame infrared image was proposed based on multi-thread optimization to correct the strip-type Fixed Pattern Noise(FPN) generated by non-uniformity response from an infrared camera. Firstly

the plus-relationship of the output value and the noise was determined by NUC algorithm based on total variation theory and a mathematical model was established to describe the strip noise. The image was split into a high-frequency component and a low-frequency component by filters

and the strip noise was fitted from the high-frequency component by the mathematical model. Finally

the real infrared image was restored by the plus-relationship obtained from the total variation theory. To improve the computing speed

the proposed algorithm was optimized with the multi-thread. The image quality evaluation of the proposed algorithm was carried out and its performance was compared with the Midway Infrared Equalization(MIRE) and the bilateral filter methods. Experimental results indicate that the proposed algorithm has reduced the response non-uniformity by 3% as compaired with original image according to national standard GB/T 17444-1998

and its efficiency is no difference with that of the MIRE. Moreover

system running time for an 14 bit infrared image with a resolution of 320×256 is 1.5 ms/frame

which meets the engineering requirements.

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references

刘永进,朱红,赵亦工. 基于红外焦平面阵列读出结构的非均匀校正算法[J]. 光学精密工程,2008,16(1):128-133.

LIU Y J, ZHU H, ZHAO Y G. Nonuniformity correction algorithm based on infrared focal plane array readout architecture[J]. Opt. Precision Eng., 2008, 16(1):128-133.(in Chinese)

曹扬,金伟其,刘崇亮,等. 红外焦平面阵列的自适应非均匀性校正及硬件实现[J]. 光学精密工程,2011,19(12):2985-2991.

CAO Y, JIN W Q, LIU CH L, et al.. Adaptive nonuniformity correction and hardware implementation of IRFPA[J]. Opt. Precision Eng., 2011, 19(12):2985-2991.(in Chinese)

代少升,张新立. 利用单片可编程系统对红外焦平面阵列进行实时非均匀性校正[J]. 光学精密工程,2009,17(4):854-858.

DAI SH SH, ZHANG X L. Real-time nonuniformity correction of infrared focal plane arrays by system on programmable chip[J]. Opt. Precision Eng., 2009,17(4):854-858.(in Chinese)

张红辉,罗海波,余新荣,等. 多点标定的自适应非均匀性校正方法[J]. 红外与激光工程,2014,43(11):3651-3654.

ZHANG H H,LUO H B,YU X R,et al.. Adaptive non-uniformity correction algorithm based on multi-point correction[J]. Infrared and Laser Engineering, 2014,43(11):3651-3654.(in Chinese)

代少升,张天骐. 基于非线性响应的红外焦平面阵列非均匀性自适应校正算法[J]. 光学精密工程,2008,16(4):714-719.

DAI SH SH, ZHANG T Q. Adaptive nonuniformity correction algorithm for IRFPA with nonlinear response[J]. Opt. Precision Eng., 2008,16(4):714-719.(in Chinese)

NING L,JUN X.Inter frame phase-correlated registration scene-based nonuniformity correction technology[J].Infrared Physics & Technology, 2015,69:198-205.

PABLO M, GUILLWEMO M, SERGIO T, et al.. Simultaneous digital super-resolution and nonuniformity correction for infrared imaging system[J].Applied Optics, 2015,54(21):6508-6515.

FAN F, MA Y, HUANG J,et al.. A combined temporal and spatial deghosting technique in scene based nonuniformity correction[J].Infrared Physics & Technology, 2015,71:408-415.

段程鹏,刘伟,谢庆胜,等. 改进的神经网络非均匀校正算法的研究与实现[J]. 光学学报,2015,35(7):0710001.

DUAN CH P, LIU W, XIE Q SH, et al.. Research and implementation of improved neural network non-uniformity correction algorithm[J]. Acta Optica Sinica, 2015, 35(7):0710001.(in Chinese)

TORRES J,INFANTE S O.Wavelet analysis for the elimination of striping noise in satellite images[J].Opt. Eng.,2001,40(7):1309-1314.

潘科辰,顾国华,陈钱,等. 一种改进的全变分模型校正红外焦平面阵列条纹非均匀性[J]. 红外技术,2016,38(2):138-143.

PAN K CH,GU G H,CHEN Q,et al.. An improved total variation model for correcting the strip nonuniformity in IRFPA image[J]. Infrared Technology, 2016, 38(2):138-143.(in Chinese)

王书朋,高腾. 基于双边滤波器的红外图像条纹噪声消除算法[J]. 红外技术,2014,36(9):728-731.

WANG SH P,GAO T. Destriping method for infrared image based on bilateral filter[J]. Infrared Technology, 2014, 36(9):728-731.(in Chinese)

张磊,王岳环,宋琼. 红外焦平面条带状非均匀校正硬件实现方法[J]. 红外与激光工程,2015,44(10):2886-2890.

ZHANG L, WANG Y H, SONG Q. Hardware implementation of stripe non-uniformity correction method of infrared focal plane arrays[J]. Infrared and Laser Engineering, 2015, 44(10):2886-2890.(in Chinese)

TENDERO Y,LANDEAU S,GILLES J.Non-uniformity correction of infrared images by midway equalization[J].IPOL, 2012:doi:10.5201/ipol.2012.

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