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Open Access

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

College of Software, Taiyuan University of Technology, Taiyuan 030024, China
College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
Information Technology Department, Shanxi Tizones Technology Co., Ltd, Taiyuan 030024, China
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Abstract

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.

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Tsinghua Science and Technology
Pages 736-748
Cite this article:
Zhang L, Liu J, Shang F, et al. Robust Segmentation Method for Noisy Images Based on an Unsupervised Denosing Filter. Tsinghua Science and Technology, 2021, 26(5): 736-748. https://doi.org/10.26599/TST.2021.9010021

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Received: 27 February 2021
Accepted: 12 March 2021
Published: 20 April 2021
© The author(s) 2021

© The author(s) 2021. The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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