Texture Synthesis by Non-Parametric Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Unsupervised, Information-Theoretic, Adaptive Image Filtering for Image Restoration
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Non-stationary analysis on datasets and applications
Non-stationary analysis on datasets and applications
A new time-scale adaptive denoising method based on wavelet shrinkage
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Robust NL-means filter with optimal pixel-wise smoothing parameter for statistical image denoising
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Diffusion Interpretation of Nonlocal Neighborhood Filters for Signal Denoising
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Iterative weighted maximum likelihood denoising with probabilistic patch-based weights
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Iterated nonlocal means for texture restoration
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Uniform and textured regions separation in natural images towards MPM adaptive denoising
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Non-local image smoothing by applying anisotropic diffusion PDE's in the space of patches
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Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
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The SURE-LET Approach to Image Denoising
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Efficient Nonlocal Means for Denoising of Textural Patterns
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Nonlocal Means With Dimensionality Reduction and SURE-Based Parameter Selection
IEEE Transactions on Image Processing
Patch reprojections for Non-Local methods
Signal Processing
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This paper deals with the parameter choice for the nonlocal means (NLM) algorithm. After basic computations on toy models highlighting the bias of the NLM, we study the bias-variance trade-off of this filter so as to highlight the need of a local choice of the parameters. Relying on Stein's unbiased risk estimate, we then propose an efficient algorithm to locally set these parameters, and we compare this method with the NLM with optimal global parameter.