Infrared super-resolution imaging based on compressed sensing

被引:10
|
作者
Sui, Xiubao [1 ]
Chen, Qian [1 ,2 ]
Gu, Guohua [1 ,2 ]
Shen, Xuewei [1 ]
机构
[1] NUST, Sch Elect Engn & Optoelect Technol, Nanjing 210094, Jiangsu, Peoples R China
[2] NUST, Key Lab Photoelect Imaging Technol & Syst, Nanjing 210094, Jiangsu, Peoples R China
基金
高等学校博士学科点专项科研基金; 中国博士后科学基金;
关键词
IRFPA; Super-resolution reconstruction; Compressed sensing; Nyquist sampling theorem; Phase mask; Complementary matching pursuit;
D O I
10.1016/j.infrared.2013.12.022
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
The theoretical basis of traditional infrared super-resolution imaging method is Nyquist sampling theorem. The reconstruction premise is that the relative positions of the infrared objects in the low-resolution image sequences should keep fixed and the image restoration means is the inverse operation of ill-posed issues without fixed rules. The super-resolution reconstruction ability of the infrared image, algorithm's application area and stability of reconstruction algorithm are limited. To this end, we proposed super-resolution reconstruction method based on compressed sensing in this paper. In the method, we selected Toeplitz matrix as the measurement matrix and realized it by phase mask method. We researched complementary matching pursuit algorithm and selected it as the recovery algorithm. In order to adapt to the moving target and decrease imaging time, we take use of area infrared focal plane array to acquire multiple measurements at one time. Theoretically, the method breaks though Nyquist sampling theorem and can greatly improve the spatial resolution of the infrared image. The last image contrast and experiment data indicate that our method is effective in improving resolution of infrared images and is superior than some traditional super-resolution imaging method. The compressed sensing super-resolution method is expected to have a wide application prospect. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:119 / 124
页数:6
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