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

By Wei He, Hongyan Zhang, Liangpei Zhang, Huanfeng Shen. IEEE Transactions on Geoscience and Remote Sensing, 2016

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Noise Reduction in Hyperspectral Imagery: Overview and Application (2018, methodology): “In [61], a denoising method was presented by adding a TV penalty to the denoising criterion (34):”
Hyperspectral Image Denoising via Tensor Low-Rank Prior and Unsupervised Deep Spatial–Spectral Prior (2022, background): “In view of the piece-wise smooth structure of HSIs, He et al. [22] proposed a total variation (TV)-regularized low-rank matrix decomposition model for HSI denoising, which obtains the inspiring performance.”
Kronecker-Basis-Representation Based Tensor Sparsity and Its Applications to Tensor Recovery (2018, background): “…by adding more prior knowledge related to data cases together with matrix low-rankness [16], [22], such as joint trace/TV based MSI recovery [23], [27] and joint low-rank/sparsity based matrix recovery [55], [56], etc. Matrix completion (MC) and matrix-based robust principal component analysis…”
Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and Sparse Representations (2018, methodology): “LRTV models stripes as sparse noise, exploits spectral low-rank property via nuclear norm, and promotes spatial piecewise smoothness via TV regularization.”