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Data-driven approaches show tremendous potential for lighting up the image brightness while preserving its visual naturalness. However, these methods introduce hand-crafted holes and noise enlargement or over\/under enhancement and color deviation. For mitigating these challenging issues, this paper presents a frequency division and multiscale learning network named FDMLNet, including two subnets, DetNet and StruNet. This design first applies the guided filter to separate the high and low frequencies of authentic images, then DetNet and StruNet are, respectively, developed to process them, to fully explore their information at different frequencies. In StruNet, a feasible feature extraction module (FFEM), grouped by multiscale learning block (MSL) and a dual-branch channel attention mechanism (DCAM), is injected to promote its multiscale representation ability. In addition, three FFEMs are connected in a new dense connectivity meant to utilize multilevel features. Extensive quantitative and qualitative experiments on public benchmarks demonstrate that our FDMLNet outperforms state-of-the-art approaches benefiting from its stronger multiscale feature expression and extraction ability.<\/jats:p>","DOI":"10.3390\/s22218244","type":"journal-article","created":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T22:36:17Z","timestamp":1666910177000},"page":"8244","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["FDMLNet: A Frequency-Division and Multiscale Learning Network for Enhancing Low-Light Image"],"prefix":"10.3390","volume":"22","author":[{"given":"Haoxiang","family":"Lu","sequence":"first","affiliation":[{"name":"School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Junming","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Zhenbing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9488-8236","authenticated-orcid":false,"given":"Rushi","family":"Lan","sequence":"additional","affiliation":[{"name":"School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}]},{"given":"Xipeng","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, C., Guo, C., Han, L., Jiang, J., Cheng, M.M., Gu, J., and Loy, C.C. 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