# Hyperspectral Image Denoising with Log-Based Robust PCA

arXiv: https://arxiv.org/abs/2105.11927 · HTML: https://mlsanity.com/paper/2105.11927/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 27 references that are in the map.
- First posted on arXiv 25 May 2021.

## Built on by (most cited first, 1 of 1)

- [Deep-learning-based decomposition of overlapping-sparse images: application at the vertex of neutrino interactions](https://mlsanity.com/paper/2310.19695/) (2023)

## Abstract (arXiv)

It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs). In this paper, we propose a novel nonconvex approach to RPCA for HSI denoising, which adopts the log-determinant rank approximation and a novel ℓ2,log norm, to restrict the low-rank or column-wise sparse properties for the component matrices, respectively.For the ℓ2,log-regularized shrinkage problem, we develop an efficient, closed-form solution, which is named ℓ2,log-shrinkage operator, which can be generally used in other problems. Extensive experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method in denoising HSIs.