文献类型：期刊文献

英文题名：Bayesian compressive sensing for thermal imagery using Gaussian-Jeffreys prior

作者：Gu, Xiaojing[1];Zhou, Peng[2];Gu, Xingsheng[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai, Peoples R China

年份：2017

卷号：83

起止页码：51

外文期刊名：INFRARED PHYSICS & TECHNOLOGY

收录：;EI(收录号：20171703610599);WOS:【SCI-EXPANDED(收录号:WOS:000403512000007)】；

基金：The work was supported by National Natural Science Foundation of China under Grant Nos. 61502293, 61573144 and 61205017, the Shanghai Young Eastern Scholar Program (No. QD2016030), the Young Teachers' Training Program for Shanghai College & University, and the Fundamental Research Funds for the Central Universities under Grant No. 222201717006.

语种：英文

外文关键词：Sparse estimation; Gaussian Jeffreys prior; Bayesian modeling; Thermal imagery; Noisy measurements

摘要：Recent advances have shown a great potential to explore compressive sensing (CS) theory for thermal imaging due to the capability of recovering high-resolution information from low-resolution measurements. In this paper, we present a Bayesian CS reconstruction algorithm that makes use of a new sparsity-inducing prior, referred as Gaussian-Jeffreys prior, and demonstrate performance gain of imposing this new prior on thermal imagery where the signal-to-noise ratio is low. We first derive a hierarchical representation of the Gaussian Jeffreys prior that facilitates computational tractability, then propose an efficient evidence approximation inference algorithm. We show that the proposed estimator is able to provide stronger sparsity-inducing power comparing to the conventional choices. Extensive numerical examples are provided with performance comparisons of different CS estimators, in particular when the compressive measurements are available via thermal imaging. (C) 2017 Elsevier B.V. All rights reserved.