An approximate message passing approach for compressive hyperspectral imaging using a simultaneous low-rank and joint-sparsity prior

2016/07/12 by Yangqing Li, Li, Yangqing, Saurabh Prasad +7
Computer Science · Engineering · #FOS: Computer and information sciences #Image and Signal Denoising Methods #Information Theory (cs.IT) #Remote-Sensing Image Classification #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.1607.03380

openalex publication_date 2016/07/12 · openalex created_date 2016/07/22 · openalex updated_date 2026/07/28

Abstract

This paper considers a compressive sensing (CS) approach for hyperspectral data acquisition, which results in a practical compression ratio substantially higher than the state-of-the-art. Applying simultaneous low-rank and joint-sparse (L&S) model to the hyperspectral data, we propose a novel algorithm to joint reconstruction of hyperspectral data based on loopy belief propagation that enables the exploitation of both structured sparsity and amplitude correlations in the data. Experimental results with real hyperspectral datasets demonstrate that the proposed algorithm outperforms the state-of-the-art CS-based solutions with substantial reductions in reconstruction error.

Citations

• Single-pixel imaging via compressive sampling

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