# Just Project! Multi-Channel Despeckling, the Easy Way

arXiv: https://arxiv.org/abs/2408.11531 · HTML: https://mlsanity.com/paper/2408.11531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [High-Quality Self-Supervised Deep Image Denoising](https://mlsanity.com/paper/1901.10277/) (2019)
- [Deep Learning Meets SAR](https://mlsanity.com/paper/2006.10027/) (2020)
- [Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks](https://mlsanity.com/paper/2007.02075/) (2020)
- [Image Restoration for Remote Sensing: Overview and Toolbox](https://mlsanity.com/paper/2107.00557/) (2021)
- [As if by magic: self-supervised training of deep despeckling networks with MERLIN](https://mlsanity.com/paper/2110.13148/) (2021)
- [Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives](https://mlsanity.com/paper/2012.05508/) (2020)
- [Despeckling Polarimetric SAR Data Using a Multi-Stream Complex-Valued Fully Convolutional Network](https://mlsanity.com/paper/2103.07394/) (2021)

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

- [Closed-Form Nonlocal Shrinkage for Multiplicative Image Denoising and SAR Despeckling](https://mlsanity.com/paper/2608.15028/) (2026)

## Abstract (arXiv)

Reducing speckle fluctuations in multi-channel SAR images is essential in many applications of SAR imaging such as polarimetric classification or interferometric height estimation. While single-channel despeckling has widely benefited from the application of deep learning techniques, extensions to multi-channel SAR images are much more challenging. This paper introduces MuChaPro, a generic framework that exploits existing single-channel despeckling methods. The key idea is to generate numerous single-channel projections, restore these projections, and recombine them into the final multi-channel estimate. This simple approach is shown to be effective in polarimetric and/or interferometric modalities. A special appeal of MuChaPro is the possibility to apply a self-supervised training strategy to learn sensor-specific networks for single-channel despeckling.