基于多帧三维块匹配的泊松噪声去除
Poisson Noise Removal Using Multi-Frame 3D Block Matching
摘要：
3D块匹配滤波器是一种用于消除单帧图像中加性高斯白噪声的最新技术。目前，BM3D有四种多帧扩展。在这项工作中，我们将这些扩展与方差稳定变换（VST）消除泊松噪声。我们的评估表明，保留原始噪声模型的噪声图像的扩展，另外有一个全面的连接的二维和时间图像信息在像素和补丁级别，给出了最好的结果。此外，我们发现一个令人惊讶的变化，由于具体的应用程序的VST的四个扩展的性能。最后，我们还引入了一个简单的低通滤波作为预处理步骤，以获得最佳性能的扩展。根据峰值信噪比，这可以在输出中产生0.94 dB的显著附加改进。
英文摘要：
The 3D block matching (BM3D) filter belongs to the state-of-the-art techniques for eliminating additive white Gaussian noise from single-frame images. There exist four multi-frame extensions of BM3D as of today. In this work, we combine these extensions with a variance stabilising transformation (VST) for eliminating Poisson noise. Our evaluation reveals that the extension which retains the original noise model of the noisy images and additionally has a comprehensive connectivity of 2D and temporal image information at both pixel and patch levels, gives the best results. Additionally, we find a surprising change in performance of one the four extensions due to the specific application of the VST. Finally, we also introduce a simple low-pass filtering as a preprocessing step for the best performing extension. This can give rise to a significant additional improvement of 0.94 dB in the output according to the peak signal to noise ratio.
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