Low-rank tensor completion with spatial-spectral consistency for hyperspectral image restoration
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Condensed Matter Physics,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Link
https://link.springer.com/content/pdf/10.1007/s11801-023-2182-2.pdf
Reference14 articles.
1. ZENG H, XIE X, NING J. Hyperspectral image denoising via global spatial-spectral total variation regularized nonconvex local low-rank tensor approximation[J]. Signal processing, 2021, 178: 107805.
2. ZENG H, XIE X, CUI H, et al. Hyperspectral image restoration via global L 1–2 spatial-spectral total variation regularized local low-rank tensor recovery[J]. IEEE transactions on geoscience and remote sensing, 2020, 59(4): 3309–3325.
3. ZHENG Y B, HUANG T Z, ZHAO X L, et al. Mixed noise removal in hyperspectral image via low-fibered-rank regularization[J]. IEEE transactions on geoscience and remote sensing, 2019, 58(1): 734–749.
4. MA A, ZHONG Y, ZHAO B, et al. Semisupervised subspace-based DNA encoding and matching classifier for hyperspectral remote sensing imagery[J]. IEEE transactions on geoscience and remote sensing, 2016, 54(8): 4402–4418.
5. CAO X, ZHAO Q, MENG D, et al. Robust low-rank matrix factorization under general mixture noise distributions[J]. IEEE transactions on image processing, 2016, 25(10): 4677–4690.
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