论文标题
通过将基于贴片的过滤器与光流相结合来删除多帧高斯噪声
Removing Multi-frame Gaussian Noise by Combining Patch-based Filters with Optical Flow
论文作者
论文摘要
基于贴片的方法，例如3D块匹配（BM3D）和非本地贝叶斯（NLB）是广泛接受的过滤器，用于从单帧图像中删除高斯噪声。在这项工作中，当存在同一场景的多个帧时，我们建议这些过滤器的三个扩展。他们中的第一个在每个帧上使用参考补丁，而不是常用的单个参考框架方法，从而利用了完整的可用信息。其余两种技术使用可分离的时空过滤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，我们将所有扩展名与强大的光流计算相结合。在大多数情况下，我们提出的两个多帧过滤器都超过了现有的扩展，同时也与最先进的基于神经网络的技术具有竞争力。此外，由于其可分离的设计，这两种策略之一是最快的。
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.
