# Pan-denoising: Guided Hyperspectral Image Denoising via Weighted Represent Coefficient Total Variation

arXiv: https://arxiv.org/abs/2407.06064 · HTML: https://mlsanity.com/paper/2407.06064/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 84 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Tensor Ring Decomposition](https://mlsanity.com/paper/1606.05535/) (2016)
- [Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition](https://mlsanity.com/paper/1707.02477/) (2017)
- [Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration](https://mlsanity.com/paper/2010.12921/) (2020)
- [Guaranteed Tensor Recovery Fused Low-rankness and Smoothness](https://mlsanity.com/paper/2302.02155/) (2023)
- [Denoising Hyperspectral Image with Non-i.i.d. Noise Structure](https://mlsanity.com/paper/1702.00098/) (2017)
- [Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices](https://mlsanity.com/paper/2201.12592/) (2022)
- [Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network](https://mlsanity.com/paper/1705.00727/) (2017)
- [Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements](https://mlsanity.com/paper/1806.02511/) (2018)
- [Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation](https://mlsanity.com/paper/2211.01825/) (2022)
- [Low-rank Matrix Factorization under General Mixture Noise Distributions](https://mlsanity.com/paper/1601.01060/) (2016)
- [Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization](https://mlsanity.com/paper/2201.02812/) (2022)
- [Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and Destriping](https://mlsanity.com/paper/2404.03313/) (2024)

## Built on by (most cited first, 5 of 5)

- [Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion](https://mlsanity.com/paper/2409.01728/) (2024)
- [Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image](https://mlsanity.com/paper/2412.04201/) (2024)
- [Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration](https://mlsanity.com/paper/2407.08509/) (2024)
- [Structure Modeling Activation Free Fourier Network for Spacecraft Image Denoising](https://mlsanity.com/paper/2409.07067/) (2024)
- [S3-PnP: Plug-and-Play Denoising for Scientific Imaging with Self-Supervised Score Priors](https://mlsanity.com/paper/2610.18458/) (2026)

## Abstract (arXiv)

This paper introduces a novel paradigm for hyperspectral image (HSI) denoising, which is termed textitpan-denoising. In a given scene, panchromatic (PAN) images capture similar structures and textures to HSIs but with less noise. This enables the utilization of PAN images to guide the HSI denoising process. Consequently, pan-denoising, which incorporates an additional prior, has the potential to uncover underlying structures and details beyond the internal information modeling of traditional HSI denoising methods. However, the proper modeling of this additional prior poses a significant challenge. To alleviate this issue, the paper proposes a novel regularization term, Panchromatic Weighted Representation Coefficient Total Variation (PWRCTV). It employs the gradient maps of PAN images to automatically assign different weights of TV regularization for each pixel, resulting in larger weights for smooth areas and smaller weights for edges. This regularization forms the basis of a pan-denoising model, which is solved using the Alternating Direction Method of Multipliers. Extensive experiments on synthetic and real-world datasets demonstrate that PWRCTV outperforms several state-of-the-art methods in terms of metrics and visual quality. Furthermore, an HSI classification experiment confirms that PWRCTV, as a preprocessing method, can enhance the performance of downstream classification tasks. The code and data are available at https://github.com/shuangxu96/PWRCTV.