A Novel Segmentation-Based Video Denoising Method with Noise Level Estimation

Most of the state of the art video denoising algorithms consider additive noise model, which is often violated in practice. In this paper, two main issues are addressed, namely, segmentation-based block matching and the estimation of noise level. Different with the previous block matching methods, we present an efficient algorithm to perform the block matching in spatially-consistent segmentations of each image frame. To estimate the noise level function (NLF), which describes the noise level as a function of image brightness, we propose a fast bilateral medial filter based method. Under the assumption of short-term coherence, this estimation method is consequently extended from single frame to multi-frames. Coupling these two techniques together, we propose a segmentation-based customized BM3D method to remove colored multiplicative noise for videos. Experimental results on benchmark data sets and real videos show that our method significantly outperforms the state of the art in removing the colored multiplicative noise.