Integrations of both high resolution reconstruction and non-uniformity correction of infrared image sequence based on regularized maximum a posteriori

doi:10.1007/s12200-011-0182-5

Front Optoelec Chin

2011, Vol. 4

Issue (4) : 438-443 https://doi.org/10.1007/s12200-011-0182-5

RESEARCH ARTICLE

Integrations of both high resolution reconstruction and non-uniformity correction of infrared image sequence based on regularized maximum a posteriori

Xiu LIU, Weiqi JIN(

), Yan CHEN, Chongliang LIU, Bin LIU

School of Optoelectronics, Beijing Institute of Technology & Key Laboratory of Photo-Electronic Imaging Technology and System, Ministry of Education, Beijing 100081, China

Download: PDF(320 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

During thermal imaging, it is vital importance to obtain high-performance images that non-uniformity noise in infrared focal plane array (IRFPA) should be eliminatined and the imaging spatial resolution should be improved as far as possible. Processing algorithms related to both of them have been hot topics, and attracted more and more attention of researchers. Considering that both high-resolution restoration algorithm of image sequences and scene-based non-uniformity correction (NUC) algorithm require multi-frame image sequences of target scene with micro-displacement, an integrated processing algorithm of high-resolution image reconstruction and NUC of infrared image sequences based on regularized maximum a posteriori (MAP) is proposed. Results of simulated and experimental thermal image suggested that this algorithm can suppress random noise and eliminate non-uniformity noise effectively, and high-resolution thermal imaging can be achieved.

Keywords infrared image image sequences motion estimation non-uniformity correction (NUC) maximum a posteriori (MAP) restoration

Corresponding Author(s): JIN Weiqi,Email:jinwq@bit.edu.cn

Issue Date: 05 December 2011

Cite this article:

Xiu LIU,Weiqi JIN,Yan CHEN, et al. Integrations of both high resolution reconstruction and non-uniformity correction of infrared image sequence based on regularized maximum a posteriori[J]. Front Optoelec Chin, 2011, 4(4): 438-443.

URL:

https://academic.hep.com.cn/foe/EN/10.1007/s12200-011-0182-5
https://academic.hep.com.cn/foe/EN/Y2011/V4/I4/438

Fig.1 Imaging degradation model of infrared low-resolution image

Fig.2 Experimental results on simulated video. (a) True high-resolution image, 240×320 pixel; (b) simulated frame-one clear low-resolution image, 120×160 pixel; (c) observed frame-one low-resolution image with = 0 and = 25, 120×160 pixel; (d) restored frame using the RMAP-HR&NUC algorithm ( = 0.9737), 240×320 pixel; (e) low-resolution image of (b) using bilinear interpolation ( = 0.9266), 240×320 pixel; (f) low-resolution image of (c) using bilinear interpolation ( = 0.8809), 240×320 pixel

Fig.3 RMSE curves under different non-conformity condition. (a) RMSE curve of reconstructed image ; (b) RMSE curve of offset parameter

Fig.4 Experiment on actual infrared image sequence. (a) Observed frame-one low-resolution infrared image, 200×200 pixel; (b) restored frame-one using bilinear interpolation algorithm, 400×400 pixel; (c) restored frame using RMAP-HR &NUC algorithm for 50 iterations, 400×400 pixel

1	Liu Z G, Hu X M, Lu J. An improved neural network non-uniformity correction for IRFPA. In: Proceedings of the Society for Photo-Instrumentation Engineers , 2009, 7383: 788330
2	Sui J, Jin W Q, Dong L Q. An adaptive nonuniformity correction algorithm for infrared line scanner based on local statistics. Chinese Optics Letters , 2007, 5(2): 74–76
3	Torres S N, Hayat M M. Kalman filtering for adaptive non-uniformity correction in infrared focal-plane arrays. J Opt Soc Am A Opt Image Sci Vis. , 2003, 20(3):470–480
4	Ratliff B M, Hayat M M. An algebraic algorithm for non-uniformity correction in focal-plane arrays. . J Opt Soc Am A Opt Image Sci Vis . 2002, 19(9):1737–1747
5	Tsai R, Huang T. Multiframe image restoration and registration. Advances in Computer Vision and Image Processing , 1984, 1: 317–339
6	Su B H, Jin W Q. Super-resolution image resolution algorithm based on Poisson-Markov model. ACTA Electronica Sinica , 2003, 31(1): 41–44 (in Chinese)
7	Kim J Y, Park R H, Yang S. Super-resolution using POCS-based reconstruction with artifact reduction constraints. In: Proceedings of the Society for Photo-Instrumentation Engineers , 2005, 5960: 59605B
8	Sun G, Li Q H, Lu L. MAP algorithm to super-resolution of infrared images. In: Proceedings of the Society for Photo-Instrumentation Engineers , 2007, 6787: 67870K
9	Jonsson R. Regularization based super resolution imaging using FFT:s. In: Proceedings of the Society for Photo-Instrumentation Engineers , 2010, 5808: 122–131
10	Shen H F, Li P X, Zhang L P. Adaptive regularized MAP super-resolution reconstruction method. Geomatics and information science of Wuhan University , 2006, 31(11): 949–952 (in Chinese)
11	Armstrong E, Hayat M, Hardie R, Majeed M H. Non-uniformity correction for improved registration and high-resolution image reconstruction in IR imagery. In: Proceedings of the Society for Photo-Instrumentation Engineers , 1999, 3808: 150–161
12	Zhao W Y, Zhang C. Scene-based nonuniformity correction and enhancement: pixel statistics and subpixel motion. Journal of the Optical Society of America , 2008, 25(7): 1668–1681 doi: 10.1364/JOSAA.25.001668 pmid:18594624
13	Irani M, Peleg S. Improving resolution by image registration. CVGIP: graphical. Models and Image Process , 1991, 53(3): 231–239 doi: 10.1016/1049-9652(91)90045-L.
14	Wang Z, Bovik A C. A universal image quality index. IEEE Signal Processing Letters , 2002, 9(3): 81–84 doi: 10.1109/97.995823.
15	Torres S N, Vera E M, Rodrigo A R. Adaptive scene-based non-uniformity correction method for infrared-focal plane arrays. In: Proceedings of the Society for Photo-Instrumentation Engineers. Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XIV . 2003, 5076: 130–139

[1]	Shaosheng DAI,Zhihui DU,Haiyan XIANG,Jinsong LIU. Reconstruction algorithm of super-resolution infrared image based on human vision processing mechanism[J]. Front. Optoelectron., 2015, 8(2): 195-202.
[2]	Minghui YANG, Sihai CHEN, Xin WU, Wen FU, Zhangli HUANG. Identification and replacement of defective pixel based on Matlab for IR sensor[J]. Front Optoelec Chin, 2011, 4(4): 434-437.

Viewed

Full text

Abstract

Cited

Shared

Discussed