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

查看全文 作　　者：Ling [1]Zhang;Jianchao [1]Liu;Fangxing [2]Shang;Gang [1]Li;Juming [1]Zhao;Yueqin [3]Zhang

机构地区：[1]College of Software,Taiyuan University of Technology,Taiyuan 030024,China;[2]College of Information and Computer,Taiyuan University of Technology,Taiyuan 030024,China;[3]Information Technology Department,Shanxi Tizones Technology Co.,Ltd,Taiyuan 030024,China

出　　处：《Tsinghua Science and Technology》索引2021年第26卷第5期,共13页

基　　金：supported by the National Natural Science Foundation of China(No.61976150);the Natural Science Foundation of Shanxi Province(Nos.201901D111091 and 201801D21135)。 摘　　要：Level-set-based image segmentation has been widely used in unsupervised segmentation tasks.Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing models.Most existing methods are based on the assumption that the distribution of image noise is known or observable.However,real-time images do not meet this assumption.To bridge this gap,we propose a novel level-set-based segmentation method with an unsupervised denoising mechanism.First,a denoising filter is acquired under the unsupervised learning paradigm.Second,the denoising filter is integrated into the level-set framework to separate noise from the noisy image input.Finally,the level-set energy function is minimized to acquire segmentation contours.Extensive experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images. 关键词：image segmentation noisy image level set autoencoder

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

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