  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.
