Coding Trajectory: Enable Video Coding for Video Denoising

We introduce a novel video denoising approach which can produce a clean video by utilizing redundant image patches existed in the video frames. Previous multi-frame video denosing approaches either require image registration or employ Patch Match algorithms for the discovery of the patch redundancy. However, these computations are time-consuming and prone to errors. On the other hand, nearly all captured videos have been compressed. Such a compression can produce a rich set of block-based motion vectors that can be utilized for the redundant patch extraction, leading to the efficient video denosing. To be specific, the motion vectors and frame references can be obtained from the video coding. Given a noised frame block, we follow its motion vectors from the coding to form a trajectory and gather a set of block candidates along the routes from its nearby frames. The trajectory is referred to as Coding Trajectory. Then, the corresponding denoised block is generated by weighted fusing the block candidates with outlier rejections. A denoised frame is consisted of all the denoised blocks. We compare our method with several state-of-the-art approaches, such as VBM3D and VB-M4D, in terms of PSNR and SSIM. The experiments show that our method can achieve high quality results while runs much faster then the other approaches.