Complex diffusion on image graphs
Dohyung Seo,Baba C. Vemuri +1 more
- 07 Nov 2009
- Vol. 2009, pp 2933-2936
TL;DR: This paper develops a new variational formulation for achieving complex diffusion on color images expressed as image graphs that involves a modified harmonic map functional and is quite distinct from the Polyakov action described in earlier work by Sochen et al.
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Abstract: Complex diffusion was introduced in image processing literature as a means to achieve simultaneous denoising and enhancement of scalar valued images. In this paper, we present a novel geometric framework for achieving complex diffusion on color images expressed as image graphs. In this framework, we develop a new variational formulation for achieving complex diffusion. This formulation involves a modified harmonic map functional and is quite distinct from the Polyakov action described in earlier work by Sochen et al. Our formulation provides a framework for simultaneous (feature preserving) denoising and enhancement. We present results of comparison between the complex diffusion, and Beltrami flow all in the image graph framework.
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References
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.
Quantum Geometry of Bosonic Strings
TL;DR: In this article, a formalism for computing sums over random surfaces which arise in all problems containing gauge invariance (like QCD, three-dimensional Ising model etc.) is developed.
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A general framework for low level vision
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TL;DR: A new geometrical framework based on which natural flows for image scale space and enhancement are presented, which unifies many classical schemes and algorithms via a simple scaling of the intensity contrast, and results in new and efficient schemes.
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TL;DR: It is proved that the imaginary part is a smoothed second derivative, scaled by time, when the complex diffusion coefficient approaches the real axis, and developed two examples of nonlinear complex processes, useful in image processing.
From High Energy Physics to Low Level Vision
TL;DR: The proposed framework for image scale space, enhancement, and segmentation is presented and is demonstrated by applying it to denoise and improve gray level and color images.