Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning

Abstract

In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method. The proposed method exploits spatial and spectral correlations in HSI data using sparse Bayesian learning and compressive sensing (CS). By utilizing a simultaneously L&S data model, we employ the information of the principal components and Bayesian learning to reconstruct the hyperspectral images. The simulation results demonstrate that the proposed method is superior to LRMR and SS&LR methods in terms of reconstruction accuracy and computational burden under the same signal-to-noise tatio (SNR) and compression ratio.

Review of Matrix Rank Constraint Model for Impulse Interference Image Inpainting

“…Following this assumption, the sparse prior information was then constrained to recover the image from the noise. Due to the effectiveness of low rank and sparsity in constrained image optimization problems, image-processing schemes that combine constrained sparsity with low-rank prior information have been continuously proposed [14,15]. Some image-denoising methods use decomposition models of low rank and sparse components, such as the robust principal component analysis (RPCA) method [16], aimed at separating low-rank images from sparse interference images.…”