Spatially Adaptive Regularization in Image Segmentation

TL;DR

This paper introduces a spatially adaptive regularization method for image segmentation that adjusts regularization locally based on image features, improving feature preservation and segmentation quality.

Contribution

It extends the total-variation regularization model by incorporating local parameters derived from image decomposition and filtering techniques, solved efficiently with split Bregman iterations.

Findings

Enhanced segmentation quality in textured and smooth regions

Effective preservation of spatial features

Numerical experiments confirm improved performance

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.