Segmentation Model for Noisy and Intensity Inhomogeneity Images via Logarithmic Density Function

Ali S, Dayyan B
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引用次数: 3

Abstract

This manuscript is devoted to the study of a new image segmentation model for noisy and intensity inhomogeneity images based on logarithmic density function. Local image information is necessary for inhomogeneous images but at the same time, it is defective for noisy images as a consequence local information misguide the motion of active contour. However, the logarithmic function in our new proposed model is capable to capture minute details in images, while ignoring the noise in it which makes it robust in such kinds of images. Comparing with local Chan-Vese Model our new proposed model gives better performance treating noisy and intensity inhomogeneity images. Finally, experiments on some noisy and intensity inhomogeneity images show the robustness of our new proposed model.
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基于对数密度函数的噪声和强度非均匀性图像分割模型
本文研究了一种新的基于对数密度函数的噪声和强度非均匀性图像分割模型。局部图像信息对于非均匀图像是必要的,但对于噪声图像,局部图像信息会误导活动轮廓的运动,因此局部图像信息的提取是有缺陷的。然而,我们新提出的模型中的对数函数能够捕获图像中的微小细节,同时忽略其中的噪声,这使得它在这类图像中具有鲁棒性。与局部Chan-Vese模型相比,该模型在处理噪声和强度非均匀性图像方面具有更好的性能。最后,在一些噪声和强度不均匀的图像上进行了实验,证明了该模型的鲁棒性。
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