  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 -
which assume the sparsifying dictionaries to be data independent, and from
prior dictionary learning studies which learn the dictionary in an offline
training phase. Our proposed formulation have shown over 5 dB improvement in
PSNR over other techniques.
