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In this work we propose a technique to remove sparse impulse noise fromhyperspectral images. Our algorithm accounts for the spatial redundancy andspectral correlation of such images. The proposed method is based on therecently introduced Blind Compressed Sensing (BCS) framework, i.e. itempirically learns the spatial and spectral sparsifying dictionaries whiledenoising the images. The BCS framework differs from existing CS techniques -which assume the sparsifying dictionaries to be data independent, and fromprior dictionary learning studies which learn the dictionary in an offlinetraining phase. Our proposed formulation have shown over 5 dB improvement inPSNR over other techniques.

Impulse Denoising From Hyper-Spectral Images: A Blind Compressed Sensing Approach