Video denoising via discrete regularization on graphs

We present local and nonlocal algorithms for video denoising based on discrete regularization on graphs. The main difference between video and image denoising is the temporal redundancy in video sequences. Recent works in the literature showed that motion compensation is counter-productive for video denoising. Our algorithms do not require any motion estimation. In this paper, we consider a video sequence as a volume and not as a sequence of frames. Hence, we combine the contribution of temporal and spatial redundancies in order to obtain high quality results for videos. To enhance the denoising quality, we develop a nonlocal method that benefits from local and nonlocal regularities within the video. Experiments show that the nonlocal method outperforms the local one by preserving finer details at the expense of an increase in the computational effort. We propose an optimized method that is faster than the nonlocal approach, while producing equally attractive results.