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Implementation of the "Non-Local Bayes" (NL-Bayes) Image Denoising Algorithm
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Nonlinear total variation based noise removal algorithms
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Two-stage image denoising by principal component analysis with local pixel grouping
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Ideal Spatial Adaptation by Wavelet Shrinkage
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A Non-Local Algorithm for Image Denoising
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Texture synthesis by non-parametric sampling
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Image modeling and enhancement via structured sparse model selection
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Image denoising using scale mixtures of gaussians in the wavelet domain
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Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
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Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering
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Sparse Representation for Color Image Restoration
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Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
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Local adaptive image restoration and enhancement with the use of DFT and DCT in a running window
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Transform domain image restoration methods: review, comparison, and interpretation
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A Review of Image Denoising Algorithms, with a New One
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Learning Multiscale Sparse Representations for Image and Video Restoration
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A Nonlocal Bayesian Image Denoising Algorithm
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Bayesian-Based Iterative Method of Image Restoration*
inferredcites →https://doi.org/10.5201/ipol.2011.ys-dctopenalex-snapshot
DCT Image Denoising: a Simple and Effective Image Denoising Algorithm
inferredcites →https://doi.org/10.5201/ipol.2012.g-tvdopenalex-snapshot
Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman
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An Analysis and Implementation of the BM3D Image Denoising Method
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An Implementation and Detailed Analysis of the K-SVD Image Denoising Algorithm
inferredcites →https://doi.org/10.5244/c.25.25openalex-snapshot
Image denoising with patch based PCA: local versus global
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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