{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T02:59:42Z","timestamp":1784084382673,"version":"3.55.0"},"reference-count":6,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,2,2]]},"abstract":"<jats:p>Low-illumination image restoration has been widely used in many fields. Aiming at the problem of low resolution and noise amplification in low light environment, this paper applies style transfer of CycleGAN(Cycle-Consistent Generative Adversarial Networks) to low illumination image enhancement. In the design network structure, different convolution kernels are used to extract the features from three paths, and the deep residual shrinkage network is designed to suppress the noise after convolution. The color deviation of the image can be resolved by the identity loss of CycleGAN. In the discriminator, different convolution kernels are used to extract image features from two paths. Compared with the training and testing results of Deep-Retinex network, GLAD network, KinD and other network methods on LOL-dataset and Brightening dataset, CycleGAN based on multi-scale depth residuals contraction proposed in this experiment on LOL-dataset results image quality evaluation indicators PSNR = 24.62, NIQE = 4.9856, SSIM = 0.8628, PSNR = 27.85, NIQE = 4.7652, SSIM = 0.8753. From the visual effect and objective index, it is proved that CycleGAN based on multi-scale depth residual shrinkage has excellent performance in low illumination enhancement, detail recovery and denoising.<\/jats:p>","DOI":"10.3233\/jifs-211664","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T15:49:34Z","timestamp":1635868174000},"page":"2383-2395","source":"Crossref","is-referenced-by-count":3,"title":["Low illumination image enhancement based on multi-scale CycleGAN with deep residual shrinkage"],"prefix":"10.1177","volume":"42","author":[{"given":"Daxin","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Software, XinJiang University, Urumqi, China"},{"name":"Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yurong","family":"Qian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Software, XinJiang University, Urumqi, China"},{"name":"Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Software, XinJiang University, Urumqi, China"},{"name":"Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianeng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Software, XinJiang University, Urumqi, China"},{"name":"Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuxiang","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Software, XinJiang University, Urumqi, China"},{"name":"Key Laboratory of Software Engineering Technology, Xinjiang University, Urumqi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-211664_ref2","first-page":"1","article-title":"Deep residualshrinkage networks for fault diagnosis","volume":"99","author":"Zhao","year":"2020","journal-title":"IEEE Transactions onIndustrial Informatics"},{"key":"10.3233\/JIFS-211664_ref4","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1038\/scientificamerican1277-108","article-title":"The retinex theory of color vision","volume":"237","author":"Land","year":"1978","journal-title":"Scientific American"},{"issue":"8","key":"10.3233\/JIFS-211664_ref5","doi-asserted-by":"crossref","first-page":"2080","DOI":"10.1109\/TIP.2007.901238","article-title":"Image denoisingby sparse 3D transform-domain collaborative filtering","volume":"16","author":"Abov","year":"2007","journal-title":"IEEETransactions on Image Processing"},{"key":"10.3233\/JIFS-211664_ref6","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.patcog.2016.06.008","article-title":"LLNet: A deep autoencoderapproach to natural low-light image enhancemen","volume":"61","author":"Lore","year":"2017","journal-title":"Pattern Recognition"},{"issue":"4","key":"10.3233\/JIFS-211664_ref7","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1109\/TIP.2018.2794218","article-title":"Learning a deep singleimage contrast enhancer from multi-exposure images","volume":"27","author":"Cai","year":"2018","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.3233\/JIFS-211664_ref11","first-page":"2672","article-title":"Generative adversarialnetworks","volume":"3","author":"Goodfellow","year":"2014","journal-title":"Advances in Neural Information Processing Systems"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-211664","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:44:34Z","timestamp":1777455874000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-211664"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,2]]},"references-count":6,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/jifs-211664","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,2]]}}}