Self-Supervised Score-Based Despeckling for SAR Imagery via Log-Domain Transformation
This provides a robust and practical solution for SAR image restoration, addressing a domain-specific challenge in remote sensing.
The paper tackles the problem of speckle noise in Synthetic Aperture Radar (SAR) imagery, which degrades image quality, by introducing a self-supervised framework using score-based generative models in the log domain, resulting in significantly shorter inference times compared to existing self-supervised techniques.
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.