Mumford-Shah Regularizer with Contextual Feedback

Date

2008-7-24

Author

Erdem, Erkut
Tarı, Zehra Sibel

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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.

Subject Keywords

Variational and PDE methods, feature preserving diffusion, Structure preserving diffusion, Disambiguation in low level vision

URI

https://hdl.handle.net/11511/28576

Journal

Journal of Mathematical Imaging and Vision

DOI

https://doi.org/10.1007/s10851-008-0109-y

Collections

Department of Computer Engineering, Article

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Citation Formats

E. Erdem and Z. S. Tarı, “Mumford-Shah Regularizer with Contextual Feedback,” Journal of Mathematical Imaging and Vision, pp. 67–84, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/28576.