  Patch-based approaches such as 3D block matching (BM3D) and non-local Bayes
(NLB) are widely accepted filters for removing Gaussian noise from single-frame
images. In this work, we propose three extensions for these filters when there
exist multiple frames of the same scene. The first of them employs reference
patches on every frame instead of a commonly used single reference frame
method, thus utilizing the complete available information. The remaining two
techniques use a separable spatio-temporal filter to reduce interactions
between dissimilar regions, hence mitigating artifacts. In order to deal with
non-registered datasets we combine all our extensions with robust optical flow
computation. Two of our proposed multi-frame filters outperform existing
extensions on most occasions by a significant margin while also being
competitive with a state-of-the-art neural network-based technique. Moreover,
one of these two strategies is the fastest among all due to its separable
design.
