Self-Supervised Score-Based Despeckling for SAR Imagery via Log-Domain Transformation

TL;DR

This paper presents a self-supervised score-based method for despeckling SAR images by transforming data into the log domain, enabling effective noise removal with faster inference times.

Contribution

It introduces a novel log-domain transformation combined with score-based models for SAR despeckling, improving efficiency and robustness over prior methods.

Findings

Significantly shorter inference times compared to existing methods

Effective noise removal in SAR images with self-supervised training

Robust despeckling performance demonstrated on SAR data

Abstract

The speckle noise inherent in Synthetic Aperture Radar (SAR) imagery significantly degrades image quality and complicates subsequent analysis. Given that SAR speckle is multiplicative and Gamma-distributed, effectively despeckling SAR imagery remains challenging. This paper introduces a novel self-supervised framework for SAR image despeckling based on score-based generative models operating in the transformed log domain. We first transform the data into the log-domain and then convert the speckle noise residuals into an approximately additive Gaussian distribution. This step enables the application of score-based models, which are trained in the transformed domain using a self-supervised objective. This objective allows our model to learn the clean underlying signal by training on further corrupted versions of the input data itself. Consequently, our method exhibits significantly shorter inference times compared to many existing self-supervised techniques, offering a robust and practical solution for SAR image restoration.