Nonlocal Image and Movie Denoising

By A. Buades, Bartomeu Coll, Jean-Michel Morel. International Journal of Computer Vision, 2008

825 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Geophysics, Media Technology

View Paper: https://www.semanticscholar.org/paper/99bdad4623682f507bda77202bdc2548d19bfbad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Geophysics

In Media Technology

Citations in the corpus

The Imaging of Dynamic Multiphase Fluid Flow Using Synchrotron-Based X-ray Microtomography at Reservoir Conditions (2015, methodology): “The imageswere then passed through a 2D non-local means edge preserving filter (Buades et al. 2008, 2005) and transformed so that any small movement between scans was corrected by the process of registration.”
Image denoising with nonlocal spectral graph wavelets (2010, background): “Many extensions and variations of the original nonlocal means have been been studied [6, 7, 8].”
NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising (2015, methodology): “The mapping ψ defined by (12) has been set in NL-SAR with h = 1/3 and G the χ(2) distribution with 49 degrees of freedom (up to a change of variables, this corresponds to the set of parameters for Gaussian NL-means used in [52] with 7 × 7 patches) resulting in the curve given in Fig.”
Pore‐by‐pore capillary pressure measurements using X‐ray microtomography at reservoir conditions: Curvature, snap‐off, and remobilization of residual CO2 (2014, methodology): “After acquisition, the images were filtered using a nonlocal means edge preserving filter [Buades et al., 2005, 2008] before being corrected for any beam hardening or softening artifacts created during acquisition by subtracting a fitted second-order polynomial surface, assumed to be constant in…”
Video Denoising, Deblocking, and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms (2012, positive): “Algorithms based on this approach have been proposed for various signal-processing problems, and mainly for image denoising [4], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15].”
Image Denoising Methods. A New Nonlocal Principle (2010, background): “The noise-to-noise criterion introduced in [18] tackles the risk that, starting from pure noise, a denoising algorithm creates structured features.”
Phase-based video motion processing (2013, methodology): “We tested several denoising algorithms, namely NL-means [Buades et al. 2008], VBM3D [Dabov et al.”
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].”
Infrared Patch-Image Model for Small Target Detection in a Single Image (2013, methodology): “…of non-local self-similarity also exists commonly in natural images, which motivates many well-known non-local methods, such as the non-local method for texture synthesis [37], the non-local algorithm for image denoising [38]–[40], the non-local sparse models for image restoration [41], and so on.”
KNN Matting (2013, neutral): “As in nonlocal matting [12], our KNN matting capitalizes on the nonlocal principle [2] in constructing affinities to produce good graph clusters.”
From Local Kernel to Nonlocal Multiple-Model Image Denoising (2009, methodology): “(30) The detailed review of the nonlocal means estimates with a number of generalizations and developments are presented by Buades et al. (2005, 2008).”
Image processing of multiphase images obtained via X‐ray microtomography: A review (2014, background): “Note that the computational cost for the neighborhood search in the entire image can become excessive, so restricting the search to a certain window size (y S in equation (10)) is required [Buades et al., 2008].”
Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision (2016, methodology): “By utilizing the nonlocal self-similarity prior of images (Buades et al, 2008), the WNNP model achieves superior performance to stateof-the-art denoising algorithms.”
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification (2016, background): “Over the last few decades, various algorithms have been developed, which include non-local self-similarity (NSS) models [2], total variation (TV) approaches [22], and sparse dictionary learning models [9].”
Rank Minimization for Snapshot Compressive Imaging (2018, methodology): “In image/video processing, since the matrix formed by nonlocal similar patches in a natural image is of low rank, a series of low-rank matrix approximation methods have been proposed for various tasks [24], [45], [46], [47], [48], [49].”