Joint Mixed-Noise Removal and Compressed Sensing Reconstruction of Hyperspectral Images via Convex Optimization

摘要

Compressed sensing (CS) reconstruction is essential in capturing hyperspectral (HS) images by one-shot. However, existing approaches for compressed HS imaging assume that compressed observation is contaminated by Gaussian noise, and so they are sensitive to the other type of noise and outliers. To resolve the above problem, we propose a new methodology compressed HS imaging that can handle mixed Gaussian-sparse noise. For robust estimation, our proposed method simultaneously estimates a clean HS image and sparse noise by solving a convex optimization problem. Experimental results illustrate the utility of our proposed framework.