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• W2141359302 abstract "Often an image g(x, y) is regularized and even restored by minimizing the Mumford-Shah functional. Properties of the regularized image u(x, y) depends critically on the numerical value of the two parameters /spl alpha/ and /spl beta/ controlling smoothness and fidelity. When /spl alpha/ and /spl beta/ are constant over the image, small details are lost when an extensive filtering is used in order to remove noise. In this paper, it is shown how the two parameters /spl alpha/ and /spl beta/ can be made self-adaptive. In fact, /spl alpha/ and /spl beta/ are not constant but automatically adapt to the local scale and contrast of features in the image. In this way, edges at all scales are detected and boundaries are well-localized and preserved. In order to preserve trihedral junctions /spl alpha/ and /spl beta/ become locally small and the regularized image u(x, y) maintains sharp and well-defined trihedral junctions. Images regularized by the proposed procedure are well-suited for further processing, such as image segmentation and object recognition." @default.
• W2141359302 created "2016-06-24" @default.
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• W2141359302 date "2004-06-01" @default.
• W2141359302 modified "2023-10-01" @default.
• W2141359302 title "Self-adaptive regularization" @default.
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• W2141359302 doi "https://doi.org/10.1109/tpami.2004.15" @default.
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