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.
Removing Multi-frame Gaussian Noise by Combining Patch-based Filters with Optical Flow