MIXED NOISE REMOVAL FOR HYPERSPECTRAL IMAGES USING HYBRID SPATIO-SPECTRAL TOTAL VARIATION

This paper proposes a new mixed noise removal method using a hybrid spatio-spectral total variation (HSSTV) for hyperspectral (HS) images. HSSTV effectively evaluates spatial and spectral piecewise smoothness and would be a powerful regularization for HS image restoration. Existing mixed noise removal methods evaluate a-priori knowledge of an HS image via multiple regularizations. However, they do not appropriately evaluate spatial piecewise smoothness, resulting in oversmoothing or artifacts. Moreover, parameter settings in existing methods are troublesome tasks because multiple balancing parameters are interdependent. In contrast, thanks to HSSTV, the proposed method can restore a clean HS image while keeping sharp edges and details. In addition, parameter settings in our method are much easier than existing ones because data fidelity is imposed as hard constraints. In the experiments, we demonstrate the advantages of our proposed method over existing regularizations.