针对噪声与灰度不均，提出新型图像分割框架，提升准确率与鲁棒性。
An ICTM-RMSAV Framework for Bias-Field Aware Image Segmentation under Poisson and Multiplicative Noise
- 融合I散度与自适应总变差项，有效抑制伽马与泊松噪声。
- 通过灰度指示导出空间自适应权重，实现区域差异化扩散。
- 结合偏置场估计与优化算法，适合含复杂噪声的真实图像分割。
图像分割是图像处理的核心任务，但当图像受严重噪声干扰并存在强度不均时，现有方法性能下降。本文在迭代卷积阈值法（ICTM）框架下，提出一种变分分割模型，引入去噪项：包含I散度项和自适应总变差（TV）正则项，适用于伽马分布乘性噪声与泊松噪声。基于灰度指示器构造的空间自适应权重，引导不同强度区域的差异化扩散。为进一步解决强度不均问题，模型估计平滑变化的偏置场，提升分割精度。区域由特征函数表示，轮廓长度相应编码。为实现高效优化，将ICTM与松弛化修正标量辅助变量（RMSAV）方案结合。在合成与真实图像上进行的大量实验表明，该模型在多种噪声类型与强度不均条件下，显著优于对比方法。
原文摘要 · Abstract (English)
Image segmentation is a core task in image processing, yet many methods degrade when images are heavily corrupted by noise and exhibit intensity inhomogeneity. Within the iterative-convolution thresholding method (ICTM) framework, we propose a variational segmentation model that integrates denoising terms. Specifically, the denoising component consists of an I-divergence term and an adaptive total-variation (TV) regularizer, making the model well suited to images contaminated by Gamma--distributed multiplicative noise and Poisson noise. A spatially adaptive weight derived from a gray-level indicator guides diffusion differently across regions of varying intensity. To further address intensity inhomogeneity, we estimate a smoothly varying bias field, which improves segmentation accuracy. Regions are represented by characteristic functions, with contour length encoded accordingly. For efficient optimization, we couple ICTM with a relaxed modified scalar auxiliary variable (RMSAV) scheme. Extensive experiments on synthetic and real-world images with intensity inhomogeneity and diverse noise types show that the proposed model achieves superior accuracy and robustness compared with competing approaches.
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