Abstract

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 signi ficantly reduce the complexity of classical NLM methods. Experimental re- sults show that our system is comparable with the state of the art in removing AWGN, and signi ficantly outperforms the state of the art in removing real, struc- tured noise.