Deep Unfolded BM3D: Unrolling Non-local Collaborative Filtering into a Trainable Neural Network
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arXiv
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| Format: | Preprint |
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2025
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| author | Basim, Kerem Unal, Mehmet Ozan Ertas, Metin Yildirim, Isa |
| author_facet | Basim, Kerem Unal, Mehmet Ozan Ertas, Metin Yildirim, Isa |
| contents | Block-Matching and 3D Filtering (BM3D) exploits non-local self-similarity priors for denoising but relies on fixed parameters. Deep models such as U-Net are more flexible but often lack interpretability and fail to generalize across noise regimes. In this study, we propose Deep Unfolded BM3D (DU-BM3D), a hybrid framework that unrolls BM3D into a trainable architecture by replacing its fixed collaborative filtering with a learnable U-Net denoiser. This preserves BM3D's non-local structural prior while enabling end-to-end optimization. We evaluate DU-BM3D on low-dose CT (LDCT) denoising and show that it outperforms classic BM3D and standalone U-Net across simulated LDCT at different noise levels, yielding higher PSNR and SSIM, especially in high-noise conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12248 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Unfolded BM3D: Unrolling Non-local Collaborative Filtering into a Trainable Neural Network Basim, Kerem Unal, Mehmet Ozan Ertas, Metin Yildirim, Isa Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Block-Matching and 3D Filtering (BM3D) exploits non-local self-similarity priors for denoising but relies on fixed parameters. Deep models such as U-Net are more flexible but often lack interpretability and fail to generalize across noise regimes. In this study, we propose Deep Unfolded BM3D (DU-BM3D), a hybrid framework that unrolls BM3D into a trainable architecture by replacing its fixed collaborative filtering with a learnable U-Net denoiser. This preserves BM3D's non-local structural prior while enabling end-to-end optimization. We evaluate DU-BM3D on low-dose CT (LDCT) denoising and show that it outperforms classic BM3D and standalone U-Net across simulated LDCT at different noise levels, yielding higher PSNR and SSIM, especially in high-noise conditions. |
| title | Deep Unfolded BM3D: Unrolling Non-local Collaborative Filtering into a Trainable Neural Network |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.12248 |