Journal ArticleDOI
Image recovery using the anisotropic diffusion equation
Farah Torkamani-Azar,K.E. Tait +1 more
TLDR
A new approach for image recovery using the anisotropic diffusion equation is developed which is based on the first derivative of the signal in time embedded in family of images with different scales.Abstract:
A new approach for image recovery using the anisotropic diffusion equation is developed which is based on the first derivative of the signal in time embedded in family of images with different scales. The diffusion coefficient is determined as a function of the gradient of the signal convolved with a symmetric exponential filter. A new discrete realization is developed for the simultaneous removal of noise and preservation of edges.read more
Citations
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Book
Anisotropic diffusion in image processing
TL;DR: This work states that all scale-spaces fulllling a few fairly natural axioms are governed by parabolic PDEs with the original image as initial condition, which means that, if one image is brighter than another, then this order is preserved during the entire scale-space evolution.
Journal ArticleDOI
Review article: Edge and line oriented contour detection: State of the art
Giuseppe Papari,Nicolai Petkov +1 more
TL;DR: The main conclusion is that contour detection has reached high degree of sophistication, taking into account multimodal contour definition (by luminance, color or texture changes), mechanisms for reducing the contour masking influence of noise and texture, perceptual grouping, multiscale aspects and high-level vision information.
Journal ArticleDOI
Generalized Perona-Malik equation for image restoration
TL;DR: Generalizations of the Perona-Malik (1990) equation are introduced and an edge enhancing functional is proposed for direct edge enhancement and a number of super diffusion operators are introduced for fast and effective smoothing.
Journal ArticleDOI
Combined Curvelet Shrinkage and Nonlinear Anisotropic Diffusion
Jianwei Ma,Gerlind Plonka +1 more
TL;DR: Numerical experiments from piecewise-smooth to textured images show good performances of the proposed method to recover the shape of edges and important detailed components, in comparison to some existing methods.
Journal ArticleDOI
An improved anisotropic diffusion model for detail- and edge-preserving smoothing
Shin-Min Chao,Du-Ming Tsai +1 more
TL;DR: A new edge-preserving smoothing technique based on a modified anisotropic diffusion that can simultaneously preserve edges and fine details while filtering out noise in the diffusion process is proposed.
References
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Journal ArticleDOI
Scale-space and edge detection using anisotropic diffusion
Pietro Perona,Jitendra Malik +1 more
TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Book
Digital Picture Processing
Azriel Rosenfeld,Avinash C. Kak +1 more
TL;DR: The rapid rate at which the field of digital picture processing has grown in the past five years had necessitated extensive revisions and the introduction of topics not found in the original edition.
Book
Robot Vision
TL;DR: Robot Vision as discussed by the authors is a broad overview of the field of computer vision, using a consistent notation based on a detailed understanding of the image formation process, which can provide a useful and current reference for professionals working in the fields of machine vision, image processing, and pattern recognition.
Book ChapterDOI
Scale-space filtering
TL;DR: Scale-space filtering is a method that describes signals qualitatively, managing the ambiguity of scale in an organized and natural way.
Journal ArticleDOI
The structure of images
TL;DR: It is shown that any image can be embedded in a one-parameter family of derived images (with resolution as the parameter) in essentially only one unique way if the constraint that no spurious detail should be generated when the resolution is diminished, is applied.