SAR Despeckling via Regional Denoising Diffusion Probabilistic Model

• URL: http://arxiv.org/abs/2401.03122v1
• Date: Sat, 6 Jan 2024 04:34:46 GMT
• Title: SAR Despeckling via Regional Denoising Diffusion Probabilistic Model
• Authors: Xuran Hu, Ziqiang Xu, Zhihan Chen, Zhengpeng Feng, Mingzhe Zhu and LJubisa Stankovic
• Abstract summary: Region Denoising Diffusion Probabilistic Model (R-DDPM) based on generative models. This paper introduces a novel despeckling approach termed Region Denoising Diffusion Probabilistic Model (R-DDPM) based on generative models.
• Score: 6.154796320245652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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