Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Graph Laplacian Regularization-Reference-Cited by-同舟云学术

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Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Graph Laplacian Regularization

Published:2022-08-15 Issue: Volume: Page:
ISSN:
Container-title:2022 34th Chinese Control and Decision Conference (CCDC)
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Author:

Chen Xin¹,Yang Fang¹

Affiliation:

1. Wuhan University of Science and Technology,Engineering Research Center of Metallurgical Automation and Measurement Technology,Wuhan,China,430081

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10032843/10032844/10034018.pdf?arnumber=10034018

Reference18 articles.

1. Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation

2. Graph Laplacian Regularization for Image Denoising: Analysis in the Continuous Domain

3. Hyperspectral Image Denoising Using Local Low-Rank Matrix Recovery and Global Spatial–Spectral Total Variation

4. Total-Variation-Regularized Low-Rank Matrix Factorization for Hyperspectral Image Restoration

5. Optimal graph Laplacian regularization for natural image denoising;pang;IEEE International Conference on Acoustics Speech and Signal Processing,2015

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Effective stripe artifact removal by a variational method: application to light-sheet microscopy, FIB-SEM and remote sensing images;2024-02-07

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