High-precision algorithm for restoration of spectral imaging based on joint solution of double sparse domains
Received:March 16, 2020  Revised:May 08, 2021  download
Citation:
Hits: 4
Download times: 1
Author NameAffiliationE-mail
LIU Shi-Jie Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China
University of Chinese Academy of Sciences Beijing 100049 China 
liushjie163@163.com 
LI Chun-Lai Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China lichunlai@mail.sitp.ac.cn 
XU Rui Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China  
TANG Guo-Liang Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China
University of Chinese Academy of Sciences Beijing 100049 China 
 
WU Bing Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China
University of Chinese Academy of Sciences Beijing 100049 China 
 
XU Yan Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China
University of Chinese Academy of Sciences Beijing 100049 China
School of Information Science&
Techno1ogy ShanghaiTech University Shanghai 201210 China 
 
WANG Jian-Yu Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai 200083 China
University of Chinese Academy of Sciences Beijing 100049 China
Hangzhou Institute for Advanced Study University of Chinese Academy of SciencesHangzhou310024 China 
jywang@mail.sitp.ac.cn 
Abstract:Compressed sensing-based spectral imaging systems need to decode the sampled data by a proper algorithm to obtain the final spectral imaging data. Traditional decoding algorithms based on single sparse domain transformation will lead to loss of spectral details. Addressing this problem, a solution is proposed by using transformation of two sparse domains. A signal was decomposed into a low frequency part and a high frequency part, sparse restoration was performed according to the characteristics of different frequencies, and then decoding was performed to obtain high-precision restored signals. In data verification, the OMP algorithm was firstly used to restore the spectral information profile in the frequency domain, then the IRLS algorithm was applied to compensate the spectral details in the spatial domain. The impact of different sparse transformations on parameter settings was analyzed, and the JDSD of different algorithm combinations was tested. Test and simulation results on 500 kinds of spectral data show that the joint solution of double sparse domains can greatly improve the fidelity of spectral restoration. With a sampling rate of 20%, the SAM and GSAM indexes are increased from 0.625 and 0.515 by traditional methods to 0.817 and 0.659, respectively. In the case of 80%sampling rate, the SAM and GSAM indexes are increased from 0.863 and 0.808 of traditional methods to 0.940 and 0.897, respectively. JDSD algorithm can maintain high-precision details such as spectral absorption peaks,which is of great significance.
keywords:Spectral imaging  spectral feature recovery  computational imaging  compressed sensing
View Full Text  HTML  View/Add Comment  Download reader

《Journal of Infrared And Millimeter Waves》