Hyperspectral image destriping method based on nonlocal low-rank and total variation

Xiangyang Kong; Jiao Zhang; Hui Wang

doi:10.1117/12.3029820

21 June 2024 Hyperspectral image destriping method based on nonlocal low-rank and total variation

Xiangyang Kong, Jiao Zhang, Hui Wang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131671V (2024) https://doi.org/10.1117/12.3029820
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

Aiming at the problem of limited destriping performance caused by ignoring the non-local similarity of strips in existing methods, a hyperspectral image (HSI) destriping method based on non-local low rank tensor decomposition and total variation is proposed. This method considers the nonlocal similarity of the strips, and uses the tensor nonlocal low rank decomposition to approximate the nonlocal strips. Combining the direction and structural sparse features of the strips, they are effectively separated from the degraded image. Meanwhile, the local and nonlocal similarity of HSIs are considered together to reduce spectral distortion. Simulation results show that the proposed method accurately estimates and separates the strips, recovers the image information affected by the strips, and overcomes the problem of strip residue by considering the non-local similarity of strips and images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiangyang Kong, Jiao Zhang, and Hui Wang "Hyperspectral image destriping method based on nonlocal low-rank and total variation", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131671V (21 June 2024); https://doi.org/10.1117/12.3029820

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