Hyperspectral image denoising with multiscale low-rank matrix recovery

Zhi-hong, Huang; Li, Shutao; Hu, Fang

doi:10.1109/igarss.2017.8128235

Cited by 5 publications

(1 citation statement)

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“…However, HSI can be easily disturbed by many external factors, such as missing entries, noise, and so on, which not only degrade the visual quality of the image but also limit the precision of the subsequent image interpretation and analysis. Therefore, HSI restoration has attracted an increasing interest in recent years and various HSI restoration models have been developed [1][2][3]. Among them, considering the spatial geometric similarity and spectral correlation of HSI, low-rank prior modeling has become one of the most popular technologies [4].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation

Liu

et al. 2020

Remote Sensing

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Low-rank tensors have received more attention in hyperspectral image (HSI) recovery. Minimizing the tensor nuclear norm, as a low-rank approximation method, often leads to modeling bias. To achieve an unbiased approximation and improve the robustness, this paper develops a non-convex relaxation approach for low-rank tensor approximation. Firstly, a non-convex approximation of tensor nuclear norm (NCTNN) is introduced to the low-rank tensor completion. Secondly, a non-convex tensor robust principal component analysis (NCTRPCA) method is proposed, which aims at exactly recovering a low-rank tensor corrupted by mixed-noise. The two proposed models are solved efficiently by the alternating direction method of multipliers (ADMM). Three HSI datasets are employed to exhibit the superiority of the proposed model over the low rank penalization method in terms of accuracy and robustness.

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