Impulse denoising for hyper-spectral images: A blind compressed sensing approach

In this work we propose a technique to remove sparse impulse noise from hyperspectral images. Our algorithm accounts for the spatial redundancy and spectral correlation of such images. The proposed method is based on the recently introduced Blind Compressed Sensing (BCS) framework, i.e. it empirically learns the spatial and spectral sparsifying dictionaries while denoising the images. The BCS framework differs from existing CS techniques that employ fixed sparsifying basis; BCS also differs from prior dictionary learning studies which learn the dictionary in an offline training phase. Our proposed formulation has shown over 5 dB improvement in PSNR over other techniques.