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Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total
  Variation Approach to Hyperspectral Denoising

Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising

27 April 2022
Haijin Zeng
Shaoguang Huang
Yongyong Chen
H. Luong
Wilfried Philips
ArXivPDFHTML

Papers citing "Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising"

3 / 3 papers shown
Title
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral
  Image Denoising
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising
Haijin Zeng
Jiezhang Cao
Kai Feng
Shaoguang Huang
Hongyan Zhang
H. Luong
Wilfried Philips
ViT
36
6
0
06 May 2023
Tensor Robust Principal Component Analysis with A New Tensor Nuclear
  Norm
Tensor Robust Principal Component Analysis with A New Tensor Nuclear Norm
Canyi Lu
Jiashi Feng
Yudong Chen
Wei Liu
Zhouchen Lin
Shuicheng Yan
56
736
0
10 Apr 2018
Hyperspectral Image Restoration via Total Variation Regularized Low-rank
  Tensor Decomposition
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
Yao Wang
Jiangjun Peng
Qian Zhao
Deyu Meng
Yee Leung
Xile Zhao
65
365
0
08 Jul 2017
1