Anisotropic Spatial–Spectral Total Variation Regularized Double Low-Rank Approximation for HSI Denoising and Destriping
Author:
Affiliation:
1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Hubei Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09869662.pdf?arnumber=9869662
Reference56 articles.
1. Tensor N-tubal rank and its convex relaxation for low-rank tensor recovery
2. Double Low-Rank Matrix Decomposition for Hyperspectral Image Denoising and Destriping
3. Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train
4. L0 Gradient Regularized Low-Rank Tensor Model for Hyperspectral Image Denoising
5. Hyperspectral Image Restoration Using Weighted Group Sparsity-Regularized Low-Rank Tensor Decomposition
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2. Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping;IEEE Transactions on Geoscience and Remote Sensing;2024
3. Comparison and Evaluation of TV-Based and Low-Rank-Based Destriping Algorithms for Hyperspectral Imagery;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
4. Deep Content-Dependent 3-D Convolutional Sparse Coding for Hyperspectral Image Denoising;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
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