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}
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;