ACM Computing Surveys (CSUR)
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Image denoising with complex ridgelets
Pattern Recognition
Space-Time Adaptation for Patch-Based Image Sequence Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Patch-based video processing: a variational Bayesian approach
IEEE Transactions on Circuits and Systems for Video Technology
Image and video denoising using adaptive dual-tree discrete wavelet packets
IEEE Transactions on Circuits and Systems for Video Technology
Image sequence denoising via sparse and redundant representations
IEEE Transactions on Image Processing
Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency
IEEE Transactions on Signal Processing
A new method for varying adaptive bandwidth selection
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
The contourlet transform: an efficient directional multiresolution image representation
IEEE Transactions on Image Processing
Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images
IEEE Transactions on Image Processing
Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
IEEE Transactions on Image Processing
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We present an effective patch-based video denoising algorithm that exploits both local and nonlocal correlations. The method groups 3D shape-adaptive patches, whose surrounding cubic neighborhoods along spatial and temporal dimensions have been found similar by patch clustering. Such grouping results in 4D data structures with arbitrary shapes. Since the obtained 4D groups are highly correlated along all the dimensions, they can be represented very sparsely with a 4D shape-adaptive DCT. The noise can be effectively attenuated by transform shrinkage. Experimental results on a wide range of videos show that this algorithm provides significant improvement over the state-of-the-art denoising algorithms in terms of both objective metric and subjective visual quality.