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arXiv · 2008.02168

Spatially Adaptive Regularization in Image Segmentation

Laura Antonelli·Valentina De Simone·Daniela di Serafino
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Abstract

We modify the total-variation-regularized image segmentation model proposed by Chan, Esedoglu and Nikolova [SIAM Journal on Applied Mathematics 66, 2006] by introducing local regularization that takes into account spatial image information. We propose some techniques for defining local regularization parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique, with the aim of preventing excessive regularization in piecewise-constant or smooth regions and preserving spatial features in nonsmooth regions. We solve the modified model by using split Bregman iterations. Numerical experiments show the effectiveness of our approach.

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Laura Antonelli, Valentina De Simone, Daniela di Serafino. 2020-08-05. Spatially Adaptive Regularization in Image Segmentation. https://arxiv.org/abs/2008.02168

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