A RGB-Guided Low-Rank Method for Compressive Hyperspectral Image Reconstruction

Limin CHEN  Jing XU  Peter Xiaoping LIU  Hui YU  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E101-A    No.2    pp.481-487
Publication Date: 2018/02/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E101.A.481
Type of Manuscript: PAPER
Category: Image
hyperspectral image reconstruction,  RGB-guide,  low-rank,  auxiliary information,  compressive sensing,  

Full Text: PDF>>
Buy this Article

Compressive spectral imaging (CSI) systems capture the 3D spatiospectral data by measuring the 2D compressed focal plane array (FPA) coded projection with the help of reconstruction algorithms exploiting the sparsity of signals. However, the contradiction between the multi-dimension of the scenes and the limited dimension of the sensors has limited improvement of recovery performance. In order to solve the problem, a novel CSI system based on a coded aperture snapshot spectral imager, RGB-CASSI, is proposed, which has two branches, one for CASSI, another for RGB images. In addition, considering that conventional reconstruction algorithms lead to oversmoothing, a RGB-guided low-rank (RGBLR) method for compressive hyperspectral image reconstruction based on compressed sensing and coded aperture spectral imaging system is presented, in which the available additional RGB information is used to guide the reconstruction and a low-rank regularization for compressive sensing and a non-convex surrogate of the rank is also used instead of nuclear norm for seeking a preferable solution. Experiments show that the proposed algorithm performs better in both PSNR and subjective effects compared with other state-of-art methods.