Robust Segmentation Method for Noisy Images Based on an Unsupervised Denosing Filter

作     者：Ling Zhang Jianchao Liu Fangxing Shang Gang Li Juming Zhao Yueqin Zhang Ling Zhang;Jianchao Liu;Fangxing Shang;Gang Li;Juming Zhao;Yueqin Zhang

作者机构：College of SoftwareTaiyuan University of TechnologyTaiyuan 030024China College of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China Information Technology DepartmentShanxi Tizones Technology Co.LtdTaiyuan 030024China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2021年第26卷第5期

页      面：736-748页

核心收录：

学科分类：080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by the National Natural Science Foundation of China(No.61976150) the Natural Science Foundation of Shanxi Province(Nos.201901D111091 and 201801D21135) 

主　　题：image segmentation noisy image level set autoencoder 

摘      要：Level-set-based image segmentation has been widely used in unsupervised segmentation *** have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing *** existing methods are based on the assumption that the distribution of image noise is known or ***,real-time images do not meet this *** bridge this gap,we propose a novel level-set-based segmentation method with an unsupervised denoising ***,a denoising filter is acquired under the unsupervised learning ***,the denoising filter is integrated into the level-set framework to separate noise from the noisy image ***,the level-set energy function is minimized to acquire segmentation *** experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images.