Destriping Remote Sensing Image via Low-Rank Approximation and Nonlocal Total Variation

Stripe noise removal is a fundamental problem in remote sensing image processing. Many efforts have been made to resolve this problem. Recently, a state-of-the-art method was proposed from image-decomposition perspective. This method argued that the stripe and clear image can be simultaneously estimated by modeling the directional structure of stripes and the local smoothness of remote sensing images. However, the potential of this method cannot be fully delivered when confronting with dense stripes with high intensity. In this letter, we further consider the nonlocal self-similarity of image patches in the spatiospectral volume in terms of nonlocal total variation and propose a method of better robustness to dense stripes. Experimental results on both synthetic and real multispectral data show that the proposed method outperforms other competing methods in the remote sensing image destriping task.