Compressed Sensing Reconstruction of Hyperspectral Images Jointly Using Spatial Smoothing Feature and Spectral Correlation

A compressed sensing (CS) reconstruction algorithm of hyperspectral images jointly using spatial and spectral characteristics is considered. Specifically, in the sampling process, each band image is sampled by the block CS method independently. In the reconstruction process, how to utilize the spatial smoothing feature of each band image and spectral correlation between different band images to formulate the joint optimization problem is the focus of this paper. The total variation (TV) norm and multihypothesis prediction are introduced to express the spatial smoothing feature and the spectral correlation, respectively. Thus, the TV norm and the prediction residual are used as the regularization items in the reconstruction optimization problem. The resulting ill-posed problem is solved by the augmented Lagrange multiplier method and alternating direction method in an iterative way, and the implementation process of the reconstruction algorithm is presented. Experimental results on four hyperspectral datasets reveal that the proposed algorithm significantly outperforms alternative strategies in terms of peak signal-to-noise ratio as well as visual quality. (C) 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.