基于偏移场校正的图像分割问题

doi:10.6040/j.issn.1671-9352.0.2025.392

摘要/Abstract

摘要： 针对低对比度图像分割中灰度不均匀性导致的性能下降问题,本文提出图像分割和偏移场校正联合模型,实现对乘性偏移场,加性偏移场和真实图像的联合估计。在此基础上,设计一种交替极小化方法(alternating minimization method, ADM)来求解此类含多个未知函数的变分泛函极小化问题。在给定条件下,证明ADM方法的收敛性。实验结果表明,本文所构建的图像分割方法在处理低对比度、边界模糊及灰度不均匀图像时具有显著优势。

关键词: 图像分割, 低对比度, 偏移场校正, 凸优化, 交替极小化

Abstract: To address the performance degradation caused by intensity inhomogeneity in low-contrast image segmentation, this paper proposes a joint model for image segmentation and bias field correction, which achieves simultaneous estimationof the multiplicative bias field, additive bias field, and the true image. On this basis, an alternating minimization method(ADM)is designed to solve the variational functional minimization problem involving multiple unknown functions. Under given conditions, we prove the convergence of the proposed ADM. Experimental results demonstrate that the proposed image segmentation method has significant advantages in handling low-contrast, blurred boundary, and intensity inhomogeneous images.

Key words: image segmentation, low-contrast, bias field correction, convex optimization, alternating minimization

中图分类号:

TP391

阮平,查远皓. 基于偏移场校正的图像分割问题[J]. 《山东大学学报(理学版)》, 2026, 61(6): 64-79.

RUAN Ping, ZHA Yuanhao. Image segmentation based on bias field correction[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2026, 61(6): 64-79.

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