SDALIE-GAN:用于弱光图像增强的结构和细节感知GAN

Youxin Pang, Mengke Yuan, Yuchun Chang, Dong‐Ming Yan
{"title":"SDALIE-GAN:用于弱光图像增强的结构和细节感知GAN","authors":"Youxin Pang, Mengke Yuan, Yuchun Chang, Dong‐Ming Yan","doi":"10.2312/PG.20211393","DOIUrl":null,"url":null,"abstract":"We present a GAN-based network architecture for low-light image enhancement, called Structure and Detail Aware Low-light Image Enhancement GAN (SDALIE-GAN), which is trained with unpaired low/normal-light images. Specifically, complementary Structure Aware Generator (SAG) and Detail Aware Generator (DAG) are designed respectively to generate an enhanced low-light image. Besides, intermediate features from SAG and DAG are integrated through guided map supervised feature attention fusion module, and regularizes the generated samples with an appended intensity adjusting module. We demonstrate the advantages of the proposed approach by comparing it with state-of-the-art low-light image enhancement methods. CCS Concepts • Computing methodologies → Computational photography;","PeriodicalId":88304,"journal":{"name":"Proceedings. Pacific Conference on Computer Graphics and Applications","volume":"55 1","pages":"69-70"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SDALIE-GAN: Structure and Detail Aware GAN for Low-light Image Enhancement\",\"authors\":\"Youxin Pang, Mengke Yuan, Yuchun Chang, Dong‐Ming Yan\",\"doi\":\"10.2312/PG.20211393\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a GAN-based network architecture for low-light image enhancement, called Structure and Detail Aware Low-light Image Enhancement GAN (SDALIE-GAN), which is trained with unpaired low/normal-light images. Specifically, complementary Structure Aware Generator (SAG) and Detail Aware Generator (DAG) are designed respectively to generate an enhanced low-light image. Besides, intermediate features from SAG and DAG are integrated through guided map supervised feature attention fusion module, and regularizes the generated samples with an appended intensity adjusting module. We demonstrate the advantages of the proposed approach by comparing it with state-of-the-art low-light image enhancement methods. CCS Concepts • Computing methodologies → Computational photography;\",\"PeriodicalId\":88304,\"journal\":{\"name\":\"Proceedings. Pacific Conference on Computer Graphics and Applications\",\"volume\":\"55 1\",\"pages\":\"69-70\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. Pacific Conference on Computer Graphics and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2312/PG.20211393\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. Pacific Conference on Computer Graphics and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2312/PG.20211393","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

我们提出了一种基于GAN的低光图像增强网络架构,称为结构和细节感知低光图像增强GAN (SDALIE-GAN),它使用未配对的低光/正常光图像进行训练。具体来说,设计了互补的结构感知生成器(SAG)和细节感知生成器(DAG)来生成增强的弱光图像。此外,通过导图监督特征注意融合模块将SAG和DAG的中间特征进行融合,并添加强度调节模块对生成的样本进行正则化。我们通过将其与最先进的低光图像增强方法进行比较,证明了所提出方法的优点。•计算方法→计算摄影;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SDALIE-GAN: Structure and Detail Aware GAN for Low-light Image Enhancement
We present a GAN-based network architecture for low-light image enhancement, called Structure and Detail Aware Low-light Image Enhancement GAN (SDALIE-GAN), which is trained with unpaired low/normal-light images. Specifically, complementary Structure Aware Generator (SAG) and Detail Aware Generator (DAG) are designed respectively to generate an enhanced low-light image. Besides, intermediate features from SAG and DAG are integrated through guided map supervised feature attention fusion module, and regularizes the generated samples with an appended intensity adjusting module. We demonstrate the advantages of the proposed approach by comparing it with state-of-the-art low-light image enhancement methods. CCS Concepts • Computing methodologies → Computational photography;
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信