吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (1): 235-241.doi: 10.13229/j.cnki.jdxbgxb201701034

• 论文 • 上一篇    下一篇

微扫描红外成像超分辨重建

王新华1, 2, 欧阳继红1, 张广2, 何阳2   

  1. 1.吉林大学 计算机科学与技术学院,长春 130012;
    2.中国科学院长春光学精密机械与物理研究所 应用光学国家重点实验室,长春 130033
  • 收稿日期:2015-11-12 出版日期:2017-01-20 发布日期:2017-01-20
  • 通讯作者: 欧阳继红(1964-),女,教授,博士生导师.研究方向:空间推理与数据挖掘.E-mail:ouyj@jlu.edu.cn
  • 作者简介:王新华(1984-),男,助理研究员,博士研究生.研究方向:计算光学成像技术.E-mail:xinhuajlu@163.com
  • 基金资助:
    国家自然科学基金项目(61170092); 吉林省科技发展计划项目(20160209006GX).

Super-resolution reconstruction of infrared images based on micro-scanner

WANG Xin-hua1, 2, OUYANG Ji-hong1, ZHANG Guang2, HE Yang2   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2. State Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
  • Received:2015-11-12 Online:2017-01-20 Published:2017-01-20

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摘要： 为了通过低分辨率红外探测器获取高质量的图像信息,对基于微扫描成像的序列图像获取方法和超分辨图像重建算法进行了研究。首先,阐述了微扫描红外成像系统工作模式和软硬件构成。然后,提出了一种基于电机驱动的光学微扫描序列图像获取方法。最后,提出了一种基于字典学习的凸集投影算法用于超分辨图像重建。采用数值仿真和实际拍摄两种方式来验证本文算法的有效性,结果均表明本文方法能够有效地重建图像边缘细节并对噪声有较好的抑制作用。

关键词: 计算机应用, 微扫描, 凸集投影, 超分辨图像重建

Abstract: In order to acquire high quality image information through infrared detector with low resolution, a method for acquiring image sequence through a Micro-scanner Imaging (MSI) system and Super-resolution Image Reconstruction (SRIR) algorithm was investigated. First, the working mode, hardware and software composition of the MSI system were elaborated. Then, the method for acquiring the image sequence from the MSI system based on motor drive was extracted. Finally, an algorithm of Projections onto Convex Sets (POCS) based on dictionary learning was presented, which was used for SRIR. To validate the proposed method, low resolution image sequence was acquired from digital simulation and real shooting. Experimental results indicate that the proposed method could effectively restore the edge information of an image and suppress noise.

Key words: computer application, micro-scanner, projections onto convex sets(POCS), super-resolution image reconstruction(SRIR)

中图分类号: 

  • TP391.4
[1] 张东晓,鲁林,李翠华,等. 基于亚像素位移的超分辨率图像重建算法[J]. 自动化学报, 2014, 40(12):2851-2861.
Zhang Dong-xiao, Lu Lin, Li Cui-hua, et al. Super-resolution image reconstruction algorithm based on sub-pixel shift[J]. Acta Automatica Sinica,2014, 40(12): 2851-2861.
[2] Kim S P, Su W Y. Recursive high-resolution reconstruction of blurred multi-frame images[J]. IEEE Transactions on Image Processing, 1993,2(4): 534-539.
[3] Panda S S, Prasad M S R S, Jena G. POCS based super-resolution image reconstruction using an adaptive regular-ization parameter[J]. International Journal of Computer Science Issues, 2011, 8(5): 155-158.
[4] 陈健,王伟国,刘廷霞,等. 基于梯度图的快速POCS超分辨率复原算法研究[J]. 仪器仪表学报, 2015,36(2):327-338.
Chen Jian, Wang Wei-guo, Liu Ting-xia, et al. Research on fast POCS super-resolution restoration algorithm based on gradient image[J]. Chinese Journal of Scientific Instrument, 2015,36(2):327-338.
[5] 陈健,王伟国,陈长青,等. 基于区域选择的快速POCS超分辨率复原算法研究[J].电子测量与仪器学报, 2015,29(6):804-815.
Chen Jian, Wang Wei-guo, Chen Chang-qing, et al. Research on fast POCS super-resolution restoration algorithm based on region selection[J]. Journal of Electronic Measurement and Instrumentation, 2015,29(6):804-815.
[6] Zhang Xiang-jun,Wu Xiao-lin. Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation[J].IEEE Transactions on Image Processing,2008, 17(6):887-896.
[7] 宋佳伟,徐煜明,肖贤建. 基于小波变换和迭代反向投影的超分辨率算法[J]. 计算机技术与发展, 2015, 25(2):74-77.
Song Jia-wei, Xu Yu-ming, Xiao Xian-jian. A super resolution algorithm based on wavelet transform and iterative back projection [J]. Computer Technology and Development, 2015, 25(2):74-77.
[8] 吴宣沛, 谢勤岚. 基于迭代反投影的彩色图像超分辨率重建[J]. 计算机与数字工程,2015,43(6):1113-1117.
Wu Xuan-pei, Xie Qin-lan. Super-resolution of color images based on iterative back projection[J]. Computer & Digital Engineering, 2015,43(6):1113-1117.
[9] Mudenagudi U, Banerjee S, Kalra P K. Space-time super-resolution using graph-cut optimization[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 2011, 33(5):995-1008.
[10] Chantas G K, Galatsanos N P,Woods N A. Super-resolution based on fast registration and maximum a posteriori recon-struction[J]. IEEE Transactions on Image Processing, 2007,16(7): 1821-1830.
[11] 张地,何家忠. 基于特征空间的人脸超分辨率重构[J]. 自动化学报, 2012, 38(7):1145-1152.
Zhang Di, He Jia-zhong. Feature space based face super-resolution reconstruction[J]. Acta Automatica Sinica, 2012, 38(7):1145-1152.
[12] 绍乐图, 陈晨, 张红刚,等. 改进的混合MAP-POCS超分辨率图像复原算法研究[J]. 电光与控制,2015,22(2):41-45.
Shao Le-tu, Chen Chen, Zhang Hong-gang, et al. An improved hybrid MAP-POCS algorithm for super-resolution image restoration research[J]. Electronics Optics & Control, 2015,22(2): 41-45.
[13] 杨登全, 姜伟, 黄江平,等. 红外凝视成像系统中的微扫描器控制[J]. 红外技术, 2014, 36(7):556-561.
Yang Deng-quan, Jiang Wei, Huang Jiang-ping, et al. Micro-scanner control in staring infrared imaging systems[J]. Infrared Technology, 2014, 36(7):556-561.
[14] 黄燕, 沈飞, 黄整章, 等. 压电式高精度位移微扫描控制系统设计[J]. 光学精密工程, 2016, 24(10s): 454-460.
Huang Yan, Shen Fei, Huang Zheng-zhang, et al. Micro-scanning control system design for piezoelectric high-precision displacement[J]. Optics and Precision Engineering, 2016, 24(10s): 454-460.
[15] 徐明飞, 庞武斌, 徐象如, 等. 高数值孔径投影光刻物镜的光学设计[J]. 光学精密工程, 2016, 24(4): 740-746.
Xu Ming-fei, Pang Wu-bin, Xu Xiang-ru, et al. Optical design of high-numerical aperture lithographic lenses[J]. Optics and Precision Engineering, 2016, 24(4): 740-746.
[16] 孙玉宝, 韦志辉, 肖亮,等. 多形态稀疏性正则化的图像超分辨率算法[J]. 电子学报, 2010, 38(12): 2898-2903.
Sun Yu-Bao, Wei Zhi-Hui, Xiao Liang, et al. Multimorphology sparsity regularized image super resolution[J]. Acta Electronica Sinica, 2010, 38(12):2898-2903.
[17] 翟海天, 李辉, 李彬. 基于区域划分的红外超分辨率重建[J]. 光学精密工程, 2015, 23(10): 2989-2996.
Zhai Hai-tian, Li Hui, Li Bin. Infrared super resolution reconstruction based on region division[J]. Optics and Precision Engineering, 2015, 23(10): 2989-2996.
[18] Yang J, Wright J, Huang T, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11):2861-2873.
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