Reweighted Block Sparsity Regularization for Remote Sensing Images Destriping

Abstract

Destriping has attracted a lot of interest in the field of remote sensing images processing. Recent research works consider the characteristics of stripes, among which low-rankness and group sparsity regularizations are widely used. However, for real scenarios, these regularizations sometimes fail to work when the underlying assumptions are violated. Motivated by this observation, we propose a novel and flexible regularization, named as the reweighted block sparsity (RBS), for complex stripes (e.g., the partial stripes). RBS divides the stripe layer into several blocks along the stripe-perpendicular direction and boosts the group sparsity of each block, where the sparsity level is adaptively controlled by updating weights. Based on RBS, we further propose a destriping model by integrating RBS and unidirectional total variation regularization, which can better detect and remove complex stripes. Moreover, we develop an alternating direction method of multipliers algorithm to solve the proposed model. Experimental results demonstrate that the proposed method outperforms the state-of-the-art competitors qualitatively and quantitatively.