Robust Estimation Approach for NL-Means Filter
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Gaussian KD-trees for fast high-dimensional filtering
ACM SIGGRAPH 2009 papers
Exemplar-Based Interpolation of Sparsely Sampled Images
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
A Variational Framework for Non-local Image Inpainting
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Clustering-based denoising with locally learned dictionaries
IEEE Transactions on Image Processing
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Optimizing non-local means for denoising low dose CT
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Principal neighborhood dictionaries for nonlocal means image denoising
IEEE Transactions on Image Processing
Iterative weighted maximum likelihood denoising with probabilistic patch-based weights
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Ramp preserving Perona-Malik model
Signal Processing
RepFinder: finding approximately repeated scene elements for image editing
ACM SIGGRAPH 2010 papers
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Pattern Recognition
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ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Rotationally invariant similarity measures for nonlocal image denoising
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Nonlocal-means image denoising technique using robust M-estimator
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A Variational Framework for Exemplar-Based Image Inpainting
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Patch reprojections for Non-Local methods
Signal Processing
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A Bias-Variance Approach for the Nonlocal Means
SIAM Journal on Imaging Sciences
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A multiresolution framework for local similarity based image denoising
Pattern Recognition
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SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
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Gabor feature based nonlocal means filter for textured image denoising
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Edge structure preserving image denoising using OAGSM/NC statistical model
Digital Signal Processing
Image denoising using SVM classification in nonsubsampled contourlet transform domain
Information Sciences: an International Journal
A Framework for Moving Least Squares Method with Total Variation Minimizing Regularization
Journal of Mathematical Imaging and Vision
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This paper contributes two novel techniques in the context of image restoration by nonlocal filtering. First, we introduce an efficient implementation of the nonlocal means filter based on arranging the data in a cluster tree. The structuring of data allows for a fast and accurate preselection of similar patches. In contrast to previous approaches, the preselection is based on the same distance measure as used by the filter itself. It allows for large speedups, especially when the search for similar patches covers the whole image domain, i.e., when the filter is truly nonlocal. However, also in the windowed version of the filter, the cluster tree approach compares favorably to previous techniques in respect of quality versus computational cost. Second, we suggest an iterative version of the filter that is derived from a variational principle and is designed to yield nontrivial steady states. It reveals to be particularly useful in order to restore regular, textured patterns.