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.
@article{arxiv.2008.02168,
title = {Spatially Adaptive Regularization in Image Segmentation},
author = {Laura Antonelli and Valentina De Simone and Daniela di Serafino},
journal= {arXiv preprint arXiv:2008.02168},
year = {2020}
}