Temporal Denoising Mask Synthesis Network for Learning Blind Video Temporal Consistency

Recently, developing temporally consistent video-based processing techniques has drawn increasing attention due to the defective extend-ability of existing image-based processing algorithms (e.g., filtering, enhancement, colorization, etc). Generally, applying these image-based algorithms independently to each video frame typically leads to temporal flickering due to the global instability of these algorithms. In this paper, we consider enforcing temporal consistency in a video as a temporal denoising problem that removing the flickering effect in given unstable pre-processed frames. Specifically, we propose a novel model termed Temporal Denoising Mask Synthesis Network (TDMS-Net) that jointly predicts the motion mask, soft optical flow and the refining mask to synthesize the temporal consistent frames. The temporal consistency is learned from the original video and the learned temporal features are applied to reprocess the output frames that are agnostic (blind) to specific image-based processing algorithms. Experimental results on two datasets for 16 different applications demonstrate that the proposed TDMS-Net significantly outperforms two state-of-the-art blind temporal consistency approaches.