Mumford-Shah Regularizer with Contextual Feedback |
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Authors: | Erkut Erdem Sibel Tari |
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Affiliation: | 1.Department of Computer Engineering,Middle East Technical University,Ankara,Turkey |
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Abstract: | We present a simple and robust feature preserving image regularization by letting local region measures modulate the diffusivity.
The purpose of this modulation is to disambiguate low level cues in early vision. We interpret the Ambrosio-Tortorelli approximation
of the Mumford-Shah model as a system with modulatory feedback and utilize this interpretation to integrate high level information
into the regularization process. The method does not require any prior model or learning; the high level information is extracted
from local regions and fed back to the regularization step. An important characteristic of the method is that both negative
and positive feedback can be simultaneously used without creating oscillations. Experiments performed with both gray and color
natural images demonstrate the potential of the method under difficult noise types, non-uniform contrast, existence of multi-scale
patterns and textures.
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Keywords: | Variational and PDE methods Feature preserving diffusion Structure preserving diffusion Disambiguation in low level vision |
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