Publications

Song, Q; Huang, ZH; Jiang, WS; Bai, K; Liu, XY; Hu, JP (2024). Remote Sensing Images Destriping via Nonconvex Regularization and Fast Regional Decomposition. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 62, 5646915.

Abstract
Remote sensing images can accurately display the electromagnetic attribute distribution of various ground objects and can be used in many applications. Nevertheless, stripe noise frequently degrades the quality of the captured images. Most current destriping studies are capable of removing the regular stripe noise. However, their outputs often produce a stripe residual or oversmoothing effect when a complex stripe noise exists. To overcome this limitation, in this article, we propose a variational destriping model based on nonconvex regularization and variable weights. First, our model constrains the stripe sparsity using the normalized is an element of -penalty function and converts it into weighted l(1) norm by l(1) majorization method. Second, unlike previous models that use scalar smoothing weights to control the output smoothness, we propose to design adaptive vector weights to constrain the smoothness. Moreover, the proposed model introduces the region weight to deal with the extreme regions. In view of the region decomposition technique, we transform our optimization model into a series of 1-D weighted l(1) problems in different directions with a linear time solver. Then, an improved alternating direction method is employed to provide fast convergence. The experimental results show that our method achieves a better performance on image detail preservation, complex stripe removal, and a faster convergence than the state-of-the-art methods.

DOI:
10.1109/TGRS.2024.3490773

ISSN:
0196-2892