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DSEGAN: Detail and structure enhanced generative adversarial network for fundus image enhancement.

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Title: DSEGAN: Detail and structure enhanced generative adversarial network for fundus image enhancement.
Authors: Liu S; School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China; Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou 510665, China., Ouyang J; School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China., Wu X; Zhongshan Ophthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou, Guangdong, China., Xu F; Qilu Hospital, Shandong University, Jinan, China., Zhou Y; Shenzhen Institute of Information Technology, Shenzhen, China., Wang X; School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China., Zhang H; School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China., Wang K; Guangdong Zhixin New Energy Technology Co., Ltd., Guangzhou, China. Electronic address: 1586547395@qq.com., Lu X; School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China; Guangdong Provincial Key Laboratory of Intellectual Property & Big Data, Guangzhou 510665, China. Electronic address: bruda@126.com.
Source: Photodiagnosis and photodynamic therapy [Photodiagnosis Photodyn Ther] 2026 Jun; Vol. 59, pp. 105475. Date of Electronic Publication: 2026 Apr 18.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 101226123 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-1597 (Electronic) Linking ISSN: 15721000 NLM ISO Abbreviation: Photodiagnosis Photodyn Ther Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1873-1597
DOI:10.1016/j.pdpdt.2026.105475