Deblurring and Denoising of Images by Nonlocal Functionals

By Stefan Kindermann, Stanley J. Osher, Peter W. Jones. Multiscale Modeling & simulation, 2005

437 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geophysics

View Paper: https://www.semanticscholar.org/paper/a6ec7496614c4f46de38e70d8a223aab3a3bf8c8

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In Computer Vision and Pattern Recognition

In Geophysics

Citations in the corpus

Image Denoising Methods. A New Nonlocal Principle (2010, methodology): “The extension of the NL-means method to define nonlocal image-adapted differential operators and nonlocal variational methods starts with [51], which proposes to perform denoising and deblurring by a nonlocal functionals.”
A nonlocal p-Laplacian evolution equation with Neumann boundary conditions (2008, background): “Jones in [24] have studied deblurring and denoising of images by nonlocal functionals, motivated by the use of neighborhood filters [14].”
Nonlocal Operators with Applications to Image Processing (2008, methodology): “A ﬁrst variational understanding as a nonconvex minimization was given in [32].”
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising (2017, background): “(3) can also be used to describe non-local regularization functionals such as those in [10, 14, 20, 25, 44].”
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization (2010, methodology): “This NL self-similarity prior was later employed in image deblurring [8], [20] and super-resolution [41].”
A supervised patch-based approach for human brain labeling (2011, positive): “this nonlocal strategy has been studied and applied in several image processing applications such as nonlocal regularization functionals in the context of inverse problems [20], [27], [14], [29], [31] or medical image synthesis [32].”
From Local Kernel to Nonlocal Multiple-Model Image Denoising (2009, methodology): “[64] is a derivation of the nonlocal means algorithms from a variational formulation.”
Color demosaicking by local directional interpolation and nonlocal adaptive thresholding (2011, methodology): “The nonlocal means (NLM) technique has been extensively studied and effectively used in image/video denoising and restoration [21-26], and recently it has also been successfully used in CDM [15].”
Non-local Color Image Denoising with Convolutional Neural Networks (2016, background): “More recently, a different regularization paradigm has been introduced where non-local operators are employed to define new regularization functionals [10, 14, 19, 22, 40].”
Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction (2010, positive): “in [31] started to investigate the use of regularization functionals with nonlocal correlation terms for general inverse problems.”
Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling (2013, methodology): “Such kind of nonlocal similarity is very helpful to improve the image restoration output, and it has been successfully used in image denoising [31-34], deblurring [35-36] and super-resolution [37-38, 49].”
Non-local Regularization of Inverse Problems (2008, methodology): “A similar framework for image inpainting is developed by Facciolo et al. in [28], where a variational justiﬁcation for our initial choice of weights [47] is given.”
Nonlocal Linear Image Regularization and Supervised Segmentation (2007, positive): “In [35] Kindermann-Osher-Jones interpreted the NL-means and neighborhood filters in general as regularizations based on nonlocal functionals in the general form:”
Image Recovery via Nonlocal Operators (2010, background): “The nonlocal means can be regarded as one step of fixed point iteration to solve the optimality conditions of the general functional [18],”