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Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images

文献类型：期刊论文

作者Zhang, Chong1,2,3; Wang, Kun1,2,3; An, Yu1,2,3; He, Kunshan1,2,3; Tong, Tong1,2,3; Tian, Jie1,2,3,4
刊名BIOMEDICAL OPTICS EXPRESS
出版日期2019-09-01
卷号10期号:9页码:4742-4756
ISSN号2156-7085
DOI10.1364/BOE.10.004742
通讯作者Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Because of the optical properties of medical fluorescence images (FIs) and hardware limitations, light scattering and diffraction constrain the image quality and resolution. In contrast to device-based approaches, we developed a post-processing method for Fl resolution enhancement by employing improved generative adversarial networks. To overcome the drawback of fake texture generation, we proposed total gradient loss for network training. Fine-tuning training procedure was applied to further improve the network architecture. Finally, a more agreeable network for resolution enhancement was applied to actual FIs to produce sharper and clearer boundaries than in the original images. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
资助项目Ministry of Science and Technology of the People's Republic of China[2017YFA0205200] ; Ministry of Science and Technology of the People's Republic of China[2015CB755500] ; Ministry of Science and Technology of the People's Republic of China[2016YFC0103803] ; Ministry of Science and Technology of the People's Republic of China[2018YFC0910602] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Chinese Academy of Sciences[XDBS01030200] ; Chinese Academy of Sciences[YJKYYQ20180048]
WOS研究方向Biochemistry & Molecular Biology ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000484088600029
出版者OPTICAL SOC AMER
资助机构Ministry of Science and Technology of the People's Republic of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/25834]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie
作者单位1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
4.BUAA CCMU Adv Innovat Ctr Big Data Based Precis M, Beijing 100083, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Chong,Wang, Kun,An, Yu,et al. Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images[J]. BIOMEDICAL OPTICS EXPRESS,2019,10(9):4742-4756.
APA Zhang, Chong,Wang, Kun,An, Yu,He, Kunshan,Tong, Tong,&Tian, Jie.(2019).Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images.BIOMEDICAL OPTICS EXPRESS,10(9),4742-4756.
MLA Zhang, Chong,et al."Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images".BIOMEDICAL OPTICS EXPRESS 10.9(2019):4742-4756.

入库方式： OAI收割

来源：自动化研究所

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