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These shortcomings are not only subjectively annoying but also affect the performance of many computer vision systems. Enhanced low-light images can be better applied to image recognition, object detection and image segmentation. This paper proposes a novel RetinexDIP method to enhance images. Noise is considered as a factor in image decomposition using deep learning generative strategies. The involvement of noise makes the image more real, weakens the coupling relationship between the three components, avoids overfitting, and improves generalization. Extensive experiments demonstrate that our method outperforms existing methods qualitatively and quantitatively.<\/jats:p>","DOI":"10.3390\/s22155593","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T04:59:16Z","timestamp":1658897956000},"page":"5593","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7527-0003","authenticated-orcid":false,"given":"Xianjie","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Basic Sciences, Shanxi Agricultural University, Jinzhong 030801, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinming","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, G., Yang, Y., Yi, D., Kittler, J., Christmas, W., Li, S.Z., and Hospedales, T. 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