摘要
This paper introduces a novel local intensity fitting energy model for segmenting noisy and intensity inhomogeneous images. A notable feature of the proposed model is its ability to simultaneously segment the image while obtaining a denoised and inhomogeneity-corrected result. The model integrates a local clustering criterion function with a denoising mechanism, in which the total energy functional comprises three key components: a local fitting energy on the denoised image, which generates a local force to attract the segmentation contour towards the expected object boundary; an edge detector-dependent smoothing term to denoise the source image, and a length regularization ensuring precise wrapping of the segmentation contour around the target object. In addition, we employ an efficient iterative convolution-thresholding method to solve the associated energy minimization problem, ensuring energy decay at each iteration. We demonstrate the efficacy and efficiency of our proposed variational image segmentation model through numerical experiments conducted on both synthetic and real images.
原文 | ???core.languages.en_GB??? |
---|---|
文章編號 | 277 |
期刊 | Multimedia Systems |
卷 | 30 |
發行號 | 5 |
DOIs | |
出版狀態 | 已出版 - 10月 2024 |