SAR Despeckling using a Denoising Diffusion Probabilistic Model

TL;DR

This paper introduces SAR-DDPM, a novel denoising diffusion probabilistic model tailored for SAR image despeckling, which significantly improves image quality by effectively removing speckle noise through a reverse diffusion process.

Contribution

The paper presents a new diffusion-based approach for SAR despeckling, incorporating a cycle spinning inference strategy to enhance performance over existing methods.

Findings

Achieves superior quantitative despeckling results on synthetic and real SAR images.

Provides qualitative improvements in image clarity and noise reduction.

Outperforms state-of-the-art despeckling techniques in experiments.

Abstract

Speckle is a multiplicative noise which affects all coherent imaging modalities including Synthetic Aperture Radar (SAR) images. The presence of speckle degrades the image quality and adversely affects the performance of SAR image understanding applications such as automatic target recognition and change detection. Thus, SAR despeckling is an important problem in remote sensing. In this paper, we introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling. The proposed method comprises of a Markov chain that transforms clean images to white Gaussian noise by repeatedly adding random noise. The despeckled image is recovered by a reverse process which iteratively predicts the added noise using a noise predictor which is conditioned on the speckled image. In addition, we propose a new inference strategy based on cycle spinning to improve the despeckling performance. Our experiments on both synthetic and real SAR images demonstrate that the proposed method achieves significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods.