提出无需长度正则的灰度水平集框架，高效分割噪声和亮度不均图像。
From level set evolution to threshold optimization: A grayscale level set framework for image segmentation
- 用光滑图像定义灰度水平集，将PDE演化转为一维阈值搜索
- 在强噪声与亮度不均图像上实现高精度分割，计算速度显著提升
- 适合处理大尺寸图像，尤其对计算效率要求高的场景
多重退化图像的分割是图像分割领域的难题。现有水平集方法常引入长度正则项以约束分割轮廓几何形状，但该正则项易引发数值不稳定且计算成本高。本文表明，在特定光滑性约束下，长度项并非必要，并理论证明其存在会影响$| abla ϕ|=1$的性质。基于此，我们定义一类光滑图像，构建灰度水平集，提出一种针对退化图像（如强噪声、亮度不均）的快速分割框架。该框架将偏微分方程演化转化为一维阈值搜索，在大规模图像上具有显著计算速度优势。实验验证了该框架在多种退化图像上的分割性能。
原文摘要 · Abstract (English)
The segmentation of multiple degradations has been a challenging problem in the field of image segmentation. Existing level set approaches commonly adopt a length regularization term to constrain the geometric shape of the segmentation contour. However, the introduction of the length term often results in numerical instability and high computational cost. In this paper, we show that the length term is not essential under certain smoothness constraints, and theoretically prove that the presence of the length term affects the property of $|\nabla ϕ|=1$. Based on the finding, we define a class of smooth images, construct the grayscale level set, and propose a fast segmentation framework for degraded images, such as heavily noisy images and intensity inhomogeneous images. The framework transforms PDE evolution into one-dimensional threshold search, which has significant advantages in computational speed, especially on large-scale images. Experiments validate the segmentation performance of the proposed framework on various degraded images.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。