Video Denoising and Simplification Via Discrete Regularization on Graphs
In this paper, we present local and nonlocal algorithms for video denoising and simplification 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 robust method that benefits from local and nonlocal regularities within the video. We propose an optimized method that is faster than the nonlocal approach, while producing equally attractive results. The experimental results show the efficiency of our algorithms in terms of both Peak Signal to Noise Ratio and subjective visual quality.
KeywordsVideo Sequence Motion Estimation Weighted Graph Temporal Redundancy Attractive Result
Unable to display preview. Download preview PDF.
- 1.Protter, M., Elad, M.: Sparse and redundant representations and motion-estimation-free algorithm for video denoising. In: Wavelets XII. Proceedings of the SPIE, vol. 6701, p. 43 (2007)Google Scholar
- 2.Buades, A., Coll, B., Morel, J.: Denoising image sequences does not require motion estimation. In: Advanced Video and Signal Based Surveillance, 2005. IEEE Conference on AVSS 2005, pp. 70–74 (2005)Google Scholar
- 5.Elmoataz, A., Lezoray, O., Bougleux, S.: Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing. IEEE Trans. Image Processing (accepted) (to appear, 2008)Google Scholar