CASIA OpenIR  > 中国科学院分子影像重点实验室
Improved generative adversarial networks using the total gradient loss for the resolution enhancement of fluorescence images
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
Source PublicationBIOMEDICAL OPTICS EXPRESS
ISSN2156-7085
2019-09-01
Volume10Issue:9Pages:4742-4756
Abstract

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

DOI10.1364/BOE.10.004742
Indexed BySCI
Language英语
Funding ProjectChinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[XDBS01030200] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Ministry of Science and Technology of the People's Republic of China[2018YFC0910602] ; 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[2015CB755500] ; Ministry of Science and Technology of the People's Republic of China[2017YFA0205200] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Chinese Academy of Sciences[GJJSTD20170004] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61671449] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; 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] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[XDBS01030200] ; Chinese Academy of Sciences[YJKYYQ20180048]
WOS Research AreaBiochemistry & Molecular Biology ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectBiochemical Research Methods ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000484088600029
PublisherOPTICAL SOC AMER
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25834
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
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.
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