SAR Despeckling via Regional Denoising Diffusion Probabilistic Model
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916081851957248 |
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| author | Hu, Xuran Xu, Ziqiang Chen, Zhihan Feng, Zhengpeng Zhu, Mingzhe Stankovic, LJubisa |
| author_facet | Hu, Xuran Xu, Ziqiang Chen, Zhihan Feng, Zhengpeng Zhu, Mingzhe Stankovic, LJubisa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_03122 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SAR Despeckling via Regional Denoising Diffusion Probabilistic Model Hu, Xuran Xu, Ziqiang Chen, Zhihan Feng, Zhengpeng Zhu, Mingzhe Stankovic, LJubisa Computer Vision and Pattern Recognition Image and Video Processing I.4.4 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. |
| title | SAR Despeckling via Regional Denoising Diffusion Probabilistic Model |
| topic | Computer Vision and Pattern Recognition Image and Video Processing I.4.4 |
| url | https://arxiv.org/abs/2401.03122 |