Patch-based approaches such as 3D block matching and non-local Bayes are widely accepted filters for removing Gaussian noise from single-frame images. 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 spatiotemporal 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.

中文翻译：

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通过将基于补丁的滤波器与光流相结合来去除多帧高斯噪声

基于补丁的方法，例如 3D 块匹配和非局部贝叶斯，是广泛接受的用于从单帧图像中去除高斯噪声的滤波器。当同一场景存在多个帧时，我们为这些过滤器提出了三个扩展。他们中的第一个在每一帧上使用参考补丁而不是常用的单参考帧方法，从而利用完整的可用信息。其余两种技术使用可分离的时空滤波器来减少不同区域之间的相互作用，从而减轻伪影。为了处理未注册的数据集，我们将所有扩展与稳健的光流计算结合起来。我们提出的两个多帧滤波器在大多数情况下都以显着的优势优于现有的扩展，同时也与最先进的基于神经网络的技术竞争。此外，由于其可分离设计，这两种策略中的一种是最快的。