On image denoising methods

By A. Buades, Bartomeu Coll, Jean-Michel Morel. 2004

362 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f9aa29c84e9d5071c968cc3d96f67267743d98d2

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Citations in the corpus

Universal Denoising Networks : A Novel CNN Architecture for Image Denoising (2017, methodology): “Specifically, the non-local operator can be expressed as the composition of three layers : 1) a convolution layer, where we use the parametrization that we adopted earlier, which applies a linear transformation to every patch extracted from the image2, 2) a block-matching layer which forms a 3-D gro”
Image Noise Level Estimation by Principal Component Analysis (2013, background): “B LIND noise level estimation is an important image processing step, since the noise level is not always known beforehand, but many image denoising [1], [2], compression [3], and segmentation [4] algorithms take it as an input parameter; and their performance depends heavily on the accuracy of the…”
Nonlocal Linear Image Regularization and Supervised Segmentation (2007, methodology): “In [10] Buades-Coll-Morel suggested the following nonlocal filter for image denoising:”
A Fractional Laplace Equation: Regularity of Solutions and Finite Element Approximations (2015, background): “Anomalous diffusion phenomena are ubiquitous in nature [29, 35]; among the several applications of these nonlocal models let us mention image processing [8, 22, 24, 32], finance [11, 13], electromagnetic fluids [34], peridynamics [42] and porous media flow [2, 14].”
Non-local Color Image Denoising with Convolutional Neural Networks (2016, methodology): “A nonexhaustive list of these methods is the non-local means filter (NLM) [4], BM3D [7], the Learned Simultaneous Sparse Coding (LSSC) [25], and the Weighted Nuclear Norm Minimization (WNNM) [15].”
Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means (2010, methodology): “Numerous approaches to denoising MR images have been proposed including approaches based on anisotropic diffusion [15], [16], the wavelet transform [10], the nonlocal means algorithm [ 3 ]‐[5], nonparametric neighborhood statistics/estimation [17]‐[20], and -space filtering [21].”
Non-local Regularization of Inverse Problems (2008, methodology): “The non-local means filter [6] goes one step further by averaging pixels that can be arbitrary far away, using a similarity measure based on distance between patches.”
A non-local algorithm for image denoising (2005, methodology): “For a more detailed analysis on the NL-means algorithm and a more complete comparison, see [2].”
Fast image and video denoising via nonlocal means of similar neighborhoods (2005, methodology): “(Top to bottom and left to right) Original noisy image, denoised image by our algorithm, and denoised image by the original method in [2].”
Deblurring and Denoising of Images by Nonlocal Functionals (2005, methodology): “For colored images this can be even smaller [6].”
Nonlocal Operators with Applications to Image Processing (2008, methodology): “The use of patch distances in [9] followed ideas by Efros and Leung [25] for texture synthesis and completion.”