The active contour model (ACM) plays a paramount part in grasping visual properties of images and exacting targets of interest. It is overwhelming hardship for traditional ACMs to segment images with noise, intensity inhomogeneity or low contrast and consider computation speed for practical applicability. Therefore, an optimized denoised bias correction (ODBC) model incorporating the pre-piecewise fitting function and the variational denoised term into the energy function is proposed for images with low contrast and intensity inhomogeneity. An optimized gradient descent equation and an upgraded regularization term are produced to enhance the robustness and sensitivity of this model. Experiments are conducted to validate that ODBC model possesses the superiority of reliability and speed in segmenting images with inhomogeneous intensity and strengthens the robustness to initial contours. The results manifest that mIOU of ODBC model exceeds 0.9, superior to prime ACMs and deep learning schemes, in segmenting images associated and unrelated with the dataset.

中文翻译：

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具有局部预拟合功能的弱边界图像分割优化去噪偏差校正模型

主动轮廓模型（ACM）在掌握图像的视觉特性和精确感兴趣的目标方面发挥着至关重要的作用。传统的 ACM 很难分割带有噪声、强度不均匀或低对比度的图像，并考虑计算速度的实际适用性。因此，针对低对比度和强度不均匀性的图像，提出了一种优化的去噪偏差校正（ODBC）模型，将预分段拟合函数和变分去噪项合并到能量函数中。生成优化的梯度下降方程和升级的正则化项以增强该模型的鲁棒性和灵敏度。实验验证了ODBC模型在分割强度不均匀的图像时具有可靠性和速度的优势，并增强了对初始轮廓的鲁棒性。结果表明，在分割与数据集相关和无关的图像方面，ODBC 模型的 mIOU 超过 0.9，优于主要 ACM 和深度学习方案。