Adaptive discrete Laplace operator
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Nonlocal filters for removing multiplicative noise
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
A new similarity measure for nonlocal filtering in the presence of multiplicative noise
Computational Statistics & Data Analysis
Self-similarity of images in the wavelet domain in terms of ℓ2 and structural similarity (SSIM)
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Image processing tools for better incorporation of 4D seismic data into reservoir models
Journal of Computational and Applied Mathematics
Nonlinear Multilayered Representation of Graph-Signals
Journal of Mathematical Imaging and Vision
Computers & Mathematics with Applications
A general non-local denoising model using multi-kernel-induced measures
Pattern Recognition
Hi-index | 0.00 |
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.