Double-Factor Tensor Cascaded-Rank Decomposition for Hyperspectral Image Denoising

Double-Factor Tensor Cascaded-Rank Decomposition for Hyperspectral Image Denoising

Hyperspectral image (HSIs) denoising is a preprocessing step that plays a crucial role in many applications used in Earth observation missions. Low-rank tensor representation can be utilized to restore mixed-noise HSIs, such as those affected by mixed Gaussian, impulse, stripe, and deadline noises....

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Bibliographic Details
Main Authors: Jie Han, Chuang Pan, Haiyong Ding, Zhichao Zhang
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Remote Sensing
Subjects:
low rank
hyperspectral image
denoising
sparsity regularization
Online Access:https://www.mdpi.com/2072-4292/16/1/109

Internet

https://www.mdpi.com/2072-4292/16/1/109

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