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

• Published in: IEEE transactions on geoscience and remote sensing Vol. 54; no. 1; pp. 178 - 188
• Main Authors: He, Wei, Zhang, Hongyan, Zhang, Liangpei, Shen, Huanfeng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Factorization; Gaussian; Gaussian noise; Hyperspectral image (HSI); Hyperspectral imaging; Image restoration; low-rank matrix factorization; Noise; Norms; rank constraint; Regularization; restoration; Sparse matrices; Spectra; Television; total variation (TV); rank constraint; Hyperspectral image (HSI); low-rank matrix factorization; total variation (TV); restoration

In this paper, we present a spatial spectral hyperspectral image (HSI) mixed-noise removal method named total variation (TV)-regularized low-rank matrix factorization (LRTV). In general, HSIs are not only assumed to lie in a low-rank subspace from the spectral perspective but also assumed to be piec...