A high-quality video denoising algorithm based on reliable motion estimation
Author(s)Liu, Ce; Freeman, William T.
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Although the recent advances in the sparse representations of images have achieved outstanding denosing results, removing real, structured noise in digital videos remains a challenging problem. We show the utility of reliable motion estimation to establish temporal correspondence across frames in order to achieve high-quality video denoising. In this paper, we propose an adaptive video denosing framework that integrates robust optical flow into a non-local means (NLM) framework with noise level estimation. The spatial regularization in optical flow is the key to ensure temporal coherence in removing structured noise. Furthermore, we introduce approximate K-nearest neighbor matching to significantly reduce the complexity of classical NLM methods. Experimental results show that our system is comparable with the state of the art in removing AWGN, and significantly outperforms the state of the art in removing real, structured noise.
11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part III
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Computer Vision – ECCV 2010
Springer Berlin / Heidelberg
Hutchison, David et al. “A High-Quality Video Denoising Algorithm Based on Reliable Motion Estimation.” Computer Vision – ECCV 2010. Ed. Kostas Daniilidis, Petros Maragos, & Nikos Paragios. LNCS Vol. 6313. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. 706–719.
Author's final manuscript