Bayesian compressive sensing for thermal imagery using Gaussian-Jeffreys prior |
| |
Institution: | 1. Department of Psychology, Ben-Gurion University of the Negev, Beer-Sheva, Israel;2. Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva, Israel;1. State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing 100191, China;2. School of Information Science & Technology, Beijing Forestry University, Beijing 100083, China |
| |
Abstract: | 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. |
| |
Keywords: | Sparse estimation Gaussian-Jeffreys prior Bayesian modeling Thermal imagery Noisy measurements |
本文献已被 ScienceDirect 等数据库收录! |
|