Remote sensing images destriping using unidirectional hybrid total variation and nonconvex low-rank regularization

Abstract In this paper, we propose a novel model for remote sensing images destriping, which includes the Schatten 1 ∕ 2 -norm and the unidirectional first-order and high-order total variation regularization. The main idea is that the stripe layer is low-rank, and the desired image possesses smoothness across stripes. Therefore, we use the Schatten 1 ∕ 2 -norm regularization to depict the low-rankness of stripes, and use the unidirectional total variation and the unidirectional high-order total variation to guarantee the smoothness of the underlying image. We develop the alternating direction method of multipliers algorithm to solve the proposed model. Extensive experiments on synthetic and real data are reported to show the superiority of the proposed method over state-of-the-art methods in terms of both quantitative and qualitative assessments.