LI Yafeng, ZHAO Qijun, ZHANG Wenbo, FAN Pan, ZHANG Renrui, SUN Jieqi, LI Jing. A Simultaneous Cartoon-Texture Image Segmentation and Image Decomposition Method[J]. Chinese Journal of Electronics, 2020, 29(5): 906-915. doi: 10.1049/cje.2020.08.006
Citation: LI Yafeng, ZHAO Qijun, ZHANG Wenbo, FAN Pan, ZHANG Renrui, SUN Jieqi, LI Jing. A Simultaneous Cartoon-Texture Image Segmentation and Image Decomposition Method[J]. Chinese Journal of Electronics, 2020, 29(5): 906-915. doi: 10.1049/cje.2020.08.006

A Simultaneous Cartoon-Texture Image Segmentation and Image Decomposition Method

doi: 10.1049/cje.2020.08.006
Funds:  This work is supported by the National Natural Science Foundation of China (No.61971005) and the Natural Science Basic Research Plan in Shaanxi Province of China (No.2018JM6043)。
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  • Corresponding author: ZHAO Qijun (corresponding author) was born in China. He obtained the bachelor and master degrees both from Shanghai Jiao Tong University, and the Ph.D. degree from the Hong Kong Polytechnic University, all in computer science. He worked as a post-doc at Michigan State University from 2010 to 2012. He is now a computer science professor at Sichuan University and a visiting professor at Tibet University. His research lies in the fields of computer vision and biometrics. (Email:qjzhao@scu.edu.cn)
  • Received Date: 2020-04-30
  • Rev Recd Date: 2020-07-16
  • Publish Date: 2020-09-10
  • Image segmentation and image decomposition are fundamental problems in image processing. Image decomposition methods for separating images into cartoon and texture components can effectively serve different image processing tasks because different components can be respectively treated in more effective way. However, image decomposition methods are currently simply taken as an independent preprocessing step, and particularly in image segmentation different effects of cartoon and texture components have not been considered. This paper presents a novel simultaneous cartoon-texture image segmentation and image decomposition method to boost the performance of both segmentation and decomposition. We design a fast alternating optimization algorithm to solve the proposed model. Experimental results demonstrate the outstanding performance of the proposed method on both image segmentation and image decomposition.
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