发表机构
University of Münster（明斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
BMND提出维度无关的块匹配协同滤波直接泊松去噪方法，无需方差稳定变换，结合噪声感知匹配与质量守恒，在1D/2D/3D数据上提升重建质量并减少强度损失。
AI 中文摘要
科学数据的泊松去噪需要能够考虑信号相关噪声、同时适应不同数据维度并保留定量强度信息的方法。我们提出了BMND，一种针对高斯和泊松观测的块匹配与协同滤波的维度无关扩展。基于BM3D和BM4D的两阶段结构，BMND直接处理泊松数据，无需方差稳定变换，通过将噪声感知的块匹配与信号相关噪声方差在协同滤波和聚合中的传播相结合。一种维度无关的参考块遍历方案支持具有任意数量轴的数组。可选的聚合感知质量守恒在加权重叠相加后保留观测到的总强度。我们在受控噪声实验和实测荧光显微采集上，对一维生理信号、二维图像和三维体积评估了该框架。实验表明，噪声感知匹配和维纳滤波提高了重建质量，而低计数体模实验显示，通过质量守恒减少了去噪引起的强度损失。该框架提供了一种统一的、非基于学习的跨任意数据维度去噪方法，并已作为开源库发布。
英文摘要
Poisson denoising of scientific data requires methods that account for signal-dependent noise while accommodating different data dimensionalities and preserving quantitative intensity information. We present BMND, a dimension-independent extension of block matching and collaborative filtering for Gaussian and Poisson observations. Building on the two-stage structure of BM3D and BM4D, BMND processes Poisson data directly, without a variance-stabilizing transform, by combining noise-aware patch matching with propagation of signal-dependent noise variances through collaborative filtering and aggregation. A dimension-independent reference-patch traversal scheme supports arrays with an arbitrary number of axes. An optional aggregation-aware mass conservation preserves the observed total intensity after weighted overlap-add. We evaluate the framework on one-dimensional physiological signals, two-dimensional images, and three-dimensional volumes, using controlled noise experiments and measured fluorescence microscopy acquisitions. The experiments demonstrate improved reconstruction quality from noise-aware matching and Wiener filtering, while low-count phantom experiments show reduced denoising-induced intensity loss through mass conservation. The framework provides a unified, non-learning-based approach to denoising across arbitrary data dimensions and is released as an open-source library.
