Hyperspectral Images Mixed Noise Removal via Group-Tube Transform Induced Collaborative Sparsity and Low-Rankness-Reference-Cited by-同舟云学术

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Hyperspectral Images Mixed Noise Removal via Group-Tube Transform Induced Collaborative Sparsity and Low-Rankness

Published:2024-07-07 Issue: Volume: Page:7618-7621
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Li Ben-Zheng¹,Ji Teng-Yu²,Wang Jian-Li³

Affiliation:

1. University of Electronic Science and Technology of China,School of Mathematical Sciences/Research Center for Image and Vision Computing,Chengdu,P. R. China

2. Northwestern Polytechnical University,School of Mathematics and Statistics,Xian,P. R. China

3. Southwest Jiaotong University,School of Computing and Artificial Intelligence,Chengdu,P. R. China

Funder

Natural Science Foundation of Shanxi Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10641739.pdf?arnumber=10641739

Reference16 articles.

1. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

2. Tensor Robust Kernel PCA for Multidimensional Data

3. Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization

4. Hyperspectral Image Restoration Using Low-Rank Matrix Recovery

5. Snapshot Compressive Imaging Using Domain-Factorized Deep Video Prior

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