Image Denoising Via Learned Dictionaries and Sparse representation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Nonlocal Image and Movie Denoising
International Journal of Computer Vision
Clustering-based denoising with locally learned dictionaries
IEEE Transactions on Image Processing
A high-quality video denoising algorithm based on reliable motion estimation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
Combined spatial and temporal domain wavelet shrinkage algorithm for video denoising
IEEE Transactions on Circuits and Systems for Video Technology
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We present an algorithm for denoising of videos corrupted by additive i.i.d. zero mean Gaussian noise with a fixed and known standard deviation. Our algorithm is patch-based. Given a patch from a frame in the video, the algorithm collects similar patches from the same and adjacent frames. All the patches in this group are denoised using a transform-based approach that involves hard thresholding of insignificant coefficients. In this paper, the transform chosen is the higher order singular value decomposition of the group of similar patches. This procedure is repeated across the entire video in sliding window fashion. We present results on a well-known database of eight video sequences. The results demonstrate the ability of our method to preserve fine textures. Moreover we demonstrate that our algorithm, which is entirely driven by patch-similarity, can produce mean-squared error results which are comparable to those produced by state of the art techniques such as [1], as also methods such as [2] that explicitly use motion estimation before denoising.