SAR Despeckling via Regional Denoising Diffusion Probabilistic Model
Xuran Hu Ziqiang Xu Zhihan Chen Zhengpeng Feng Mingzhe Zhu LJubisa Stankovic
Speckle noise poses a significant challenge in maintaining the quality of
synthetic aperture radar (SAR) images, so SAR despeckling techniques have drawn
increasing attention. Despite the tremendous advancements of deep learning in
fixed-scale SAR image despeckling, these methods still struggle to deal with
large-scale SAR images. To address this problem, this paper introduces a novel
despeckling approach termed Region Denoising Diffusion Probabilistic Model
(R-DDPM) based on generative models. R-DDPM enables versatile despeckling of
SAR images across various scales, accomplished within a single training
session. Moreover, The artifacts in the fused SAR images can be avoided
effectively with the utilization of region-guided inverse sampling. Experiments
of our proposed R-DDPM on Sentinel-1 data demonstrates superior performance to
existing methods.