# Multispectral Image Restoration by Generalized Opponent Transformation Total Variation

arXiv: https://arxiv.org/abs/2403.12770 · HTML: https://mlsanity.com/paper/2403.12770/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 19 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [Handling noise in image deblurring via joint learning](https://mlsanity.com/paper/2001.09730/) (2020)
- [Multispectral image denoising with optimized vector non-local mean filter](https://mlsanity.com/paper/1610.06688/) (2016)

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

- [Rethinking Coupled Tensor Analysis for Hyperspectral Super-Resolution: Recoverable Modeling Under Endmember Variability](https://mlsanity.com/paper/2512.19489/) (2025)

## Abstract (arXiv)

Multispectral images (MSI) contain light information in different wavelengths of objects, which convey spectral-spatial information and help improve the performance of various image processing tasks. Numerous techniques have been created to extend the application of total variation regularization in restoring multispectral images, for example, based on channel coupling and adaptive total variation regularization. The primary contribution of this paper is to propose and develop a new multispectral total variation regularization in a generalized opponent transformation domain instead of the original multispectral image domain. Here opponent transformations for multispectral images are generalized from a well-known opponent transformation for color images. We will explore the properties of generalized opponent transformation total variation (GOTTV) regularization and the corresponding optimization formula for multispectral image restoration. To evaluate the effectiveness of the new GOTTV method, we provide numerical examples that showcase its superior performance compared to existing multispectral image total variation methods, using criteria such as MPSNR and MSSIM.