SAR Despeckling via Regional Denoising Diffusion Probabilistic Model
This addresses the problem of speckle noise in SAR images for remote sensing applications, representing an incremental improvement over existing deep learning methods.
The paper tackles the challenge of despeckling large-scale synthetic aperture radar (SAR) images by introducing a Region Denoising Diffusion Probabilistic Model (R-DDPM), which enables versatile despeckling across various scales in a single training session and demonstrates superior performance on Sentinel-1 data.
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.