An ICTM-RMSAV Framework for Bias-Field Aware Image Segmentation under Poisson and Multiplicative Noise
Xinyu Wang, Wenjun Yao, Fanghui Song and Zhichang Guo
图像分割是图像处理的核心任务,但是当图像被噪声严重损坏并表现出强度不均匀时,许多方法都会降解。 在迭代卷积阈值方法(ICTM)框架中,我们提出了一个集成去噪术语的变体分割模型。 具体来说,去噪组件由I-divergence术语和自适应总变量(TV)正则器组成,使模型非常适合被Gamma分布的乘法噪声和Poisson噪声污染的图像。 来自灰色水平指标的空间适应性权重引导不同强度区域的扩散。 为了进一步解决强度不均匀性,我们估计了一个平滑变化的偏置场,从而提高了分割精度。 区域由特征函数表示,轮廓长度相应编码。 为了进行高效的优化,我们将ICTM与轻松的修改标量辅助变量(RMSAV)方案相结合。 对具有强度不均匀和不同噪声类型的合成和真实世界图像进行的广泛实验表明,与竞争方法相比,拟议的模型实现了卓越的准确性和鲁棒性。
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–distribute...
