Poisson Noise Removal Using Multi-Frame 3D Block Matching

TL;DR

This paper enhances multi-frame 3D block matching (BM3D) techniques with variance stabilising transformation (VST) to effectively remove Poisson noise, demonstrating improved results with a simple low-pass filter as preprocessing.

Contribution

It introduces a novel combination of multi-frame BM3D extensions with VST for Poisson noise removal and identifies the most effective extension with additional preprocessing benefits.

Findings

Best extension retains original noise model and uses comprehensive 2D and temporal connectivity.

Applying low-pass filtering as preprocessing improves PSNR by 0.94 dB.

Performance of one extension changes significantly with VST application.

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.