Automatic Hyperspectral Image Restoration Using Sparse and Low-Rank Modeling

Hyperspectral restoration is a preprocessing step for hyperspectral imagery. In this letter, we propose a parameter-free method for the restoration of hyperspectral images (HSIs) called HyRes. The restoration method is based on a sparse low-rank model that uses the $\ell _{1}$ penalized least squares for estimating the unknown signal. The Stein’s unbiased risk estimator is exploited to select all the parameters of the model yielding a fully automatic (parameter free) technique. Experimental results confirm that HyRes outperforms the state-of-the-art techniques in terms of signal-to-noise ratio, structural similarity index, and spectral angle distance for a simulated data set and in terms of noise-level estimation for the real data sets used in this letter. In the experiments, it was noted that HyRes is computationally less expensive compared with competitive techniques. Therefore, HyRes can be used as a reliable automatic preprocessing step for further analysis of HSIs.