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arXiv · 2511.12248

Deep Unfolded BM3D: Unrolling Non-local Collaborative Filtering into a Trainable Neural Network

Kerem Basim·Mehmet Ozan Unal·Metin Ertas·Isa Yildirim
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Abstract

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.

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Kerem Basim, Mehmet Ozan Unal, Metin Ertas, Isa Yildirim. 2025-11-15. Deep Unfolded BM3D: Unrolling Non-local Collaborative Filtering into a Trainable Neural Network. https://arxiv.org/abs/2511.12248

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