# SAR Despeckling Using Overcomplete Convolutional Networks

arXiv: https://arxiv.org/abs/2205.15906 · HTML: https://mlsanity.com/paper/2205.15906/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 5 of 5)

- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [SAR image despeckling through convolutional neural networks](https://mlsanity.com/paper/1704.00275/) (2017)
- [SAR Image Despeckling Using a Convolutional Neural Network](https://mlsanity.com/paper/1706.00552/) (2017)
- [Exploring Overcomplete Representations for Single Image Deraining using CNNs](https://mlsanity.com/paper/2010.10661/) (2020)
- [A New Ratio Image Based CNN Algorithm For SAR Despeckling](https://mlsanity.com/paper/1906.04111/) (2019)

## Built on by (most cited first, 2 of 2)

- [SAR Despeckling using a Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2206.04514/) (2022)
- [Multitask Learning for SAR Ship Detection with Gaussian-Mask Joint Segmentation](https://mlsanity.com/paper/2411.13847/) (2024)

## Abstract (arXiv)

Synthetic Aperture Radar (SAR) despeckling is an important problem in remote sensing as speckle degrades SAR images, affecting downstream tasks like detection and segmentation. Recent studies show that convolutional neural networks(CNNs) outperform classical despeckling methods. Traditional CNNs try to increase the receptive field size as the network goes deeper, thus extracting global features. However,speckle is relatively small, and increasing receptive field does not help in extracting speckle features. This study employs an overcomplete CNN architecture to focus on learning low-level features by restricting the receptive field. The proposed network consists of an overcomplete branch to focus on the local structures and an undercomplete branch that focuses on the global structures. We show that the proposed network improves despeckling performance compared to recent despeckling methods on synthetic and real SAR images.