Poisson Noise Removal Using Multi-Frame 3D Block Matching

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Abstract
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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