A review of image denoising algorithms, with a new one

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)490-530
Journal / PublicationMultiscale Modeling and Simulation
Volume4
Issue number2
Publication statusPublished - 2005
Externally publishedYes

Abstract

The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding performance when the image model corresponds to the algorithm assumptions but fail in general and create artifacts or remove image fine structures. The main focus of this paper is, first, to define a general mathematical and experimental methodology to compare and classify classical image denoising algorithms and, second, to propose a nonlocal means (NL-means) algorithm addressing the preservation of structure in a digital image. The mathematical analysis is based on the analysis of the "method noise," defined as the difference between a digital image and its denoised version. The NL-means algorithm is proven to be asymptotically optimal under a generic statistical image model. The denoising performance of all considered methods are compared in four ways; mathematical: asymptotic order of magnitude of the method noise under regularity assumptions; perceptual-mathematical: the algorithms artifacts and their explanation as a violation of the image model; quantitative experimental: by tables of L2 distances of the denoised version to the original image. The most powerful evaluation method seems, however, to be the visualization of the method noise on natural images. The more this method noise looks like a real white noise, the better the method. © 2005 Society for Industrial and Applied Mathematics.

Research Area(s)

  • Adaptive filters, Frequency domain filters, Image restoration, Nonparametric estimation, PDE smoothing filters

Bibliographic Note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Citation Format(s)

A review of image denoising algorithms, with a new one. / Buades, A.; Coll, B.; Morel, J. M.
In: Multiscale Modeling and Simulation, Vol. 4, No. 2, 2005, p. 490-530.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review