基于可解释神经网络的协作式DCT图像去噪极限研究
A Study of the Limits of Collaborative DCT-Based Image Denoising via Interpretable Neural Networks
浏览论文内容
中文总结 AI 辅助
本研究提出DeepBM3D，将BM3D协作滤波重构为全可微神经网络，结合非局部块分组与DCT域学习维纳滤波，在低中噪声下媲美FFDNet并优于经典方法。
中文摘要 AI 辅助
图像去噪仍然是图像恢复中的一个基本问题，在摄影、生物医学和科学成像中有着广泛的应用。现代深度神经网络通过学习强大的图像先验实现了优异的性能，但往往依赖于可解释性有限的大型黑盒模型。相比之下，基于DCT的滑动窗口和协作滤波方法（如BM3D）具有清晰的算法结构，但依赖于手工设计且不可微的操作。本研究探讨了当此类结构化协作滤波原理被重新表述为可训练模型时，其性能可以达到何种程度。我们提出了DeepBM3D，一种紧凑的全可微架构，在受BM3D启发的流程中结合了非局部块分组、DCT域滤波和多阶段细化。轻量级卷积特征提取器引导块分组，而滤波则通过DCT域中学习到的维纳权重进行。实验表明，DeepBM3D优于经典和混合基线，在低和中噪声水平下与FFDNet保持竞争力，并且在重复纹理上表现尤为出色。
英文摘要
Image denoising remains a fundamental problem in image restoration, with applications in photography, biomedical, and scientific imaging. Modern deep neural networks achieve strong performance by learning powerful image priors, but often rely on large black-box models with limited interpretability. In contrast, DCT-based sliding-window and collaborative filtering methods such as BM3D offer clear algorithmic structure, but depend on handcrafted and non-differentiable operations. This work studies how far such structured collaborative filtering principles can be pushed when reformulated as trainable models. We introduce DeepBM3D, a compact fully differentiable architecture that combines non-local patch grouping, DCT-domain filtering, and multi-stage refinement within a BM3D-inspired pipeline. Lightweight convolutional feature extractors guide patch grouping, while filtering is performed through learned Wiener weights in the DCT domain. Experiments show that DeepBM3D improves over classical and hybrid baselines, remains competitive with FFDNet at low and moderate noise levels, and performs particularly well on repetitive textures.
发表机构
- Universitat de les Illes Balears（巴利阿里群岛大学）
机构由 AI 辅助整理，请以论文原文为准。
