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Zbl 1182.62184
Buades, A.; Coll, B.; Morel, J.M.
Image denoising methods. A new nonlocal principle. (English)
[J] SIAM Rev. 52, No. 1, 113-147 (2010). ISSN 0036-1445; ISSN 1095-7200/e

Summary: 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 fine structures in images. 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 is 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 $L^2$ distances of the denoised version to the original image. The fourth and perhaps most powerful evaluation method is, however, the visualization of the method noise on natural images. The more this method noise looks like a real white noise, the better the method.

MSC 2000:: *62M40 Statistics of random fields
62G05 Nonparametric estimation
62M20 Prediction, etc. (statistics)
62H35 Image analysis
65C20 Models (numerical methods)

Keywords: image restoration; nonparametric estimation; PDE smoothing filters; adaptive filters; frequency domain filters

Cited in: Zbl 1254.94010

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Zentralblatt MATH Berlin [Germany]