Non-Local Means Denoising

By A. Buades, Bartomeu Coll, Jean-Michel Morel. 2011

1,071 citations recorded by the source corpus.

We present in this paper a new denoising method called non-local means. The method is based on a simple principle: replacing the color of a pixel with an average of the colors of similar pixels. But the most similar pixels to a given pixel have no reason to be close at all. It is therefore licit to scan a vast portion of the image in search of all the pixels that really resemble the pixel one wants to denoise. The paper presents two implementations of the method and displays some results. license. A C/C++ implementation is provided. Please see the readme ﬁle or the online documentation for details.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Signal Processing

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Citations in the corpus

This Looks Like That, Because ... Explaining Prototypes for Interpretable Image Recognition (2020, methodology): “The texture modification is implemented with the Non-local Means Denoising algorithm [7] for colored images in OpenCV4.”
A Nonlocal Bayesian Image Denoising Algorithm (2013, methodology): “In particular, this can be done with TV-denoising [18], NL-means [6], BM3D [26], KSVD [27], and NL-Bayes [5].”
An introduction to continuous optimization for imaging (2016, background): “State-of-the-art denoising methods will usually revert to non-local techniques that treat patches as a whole, allowing better representation of textures (Buades et al. 2005, Buades et al. 2011, Dabov et al. 2007).”
An Analysis and Implementation of the BM3D Image Denoising Method (2012, methodology): “Like for NL-means, there is a variant with shape-adaptive patches [5].”
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems (2017, methodology): “(2) The interpretation of the proximal operator as a denoising of b motivated the authors of [41, 19] to replace the proximal operator of R by a powerful denoising method such as NLM or BM3D.”
Meta-Transfer Learning for Zero-Shot Super-Resolution (2020, neutral): “Therefore it has long been used in image restoration tasks, including image denoising [5, 6] and super-resolution [24, 14].”