Images　captured　under　low-light　environments　typically　have　poor　visibility,　affecting　many　advanced　computer　vision　tasks.　In　recent　years,　there　have　been　some　low-light　image　enhancement　models　based　on　deep　learning,　but　they　have　not　been　able　to　effectively　mine　the　deep　multiscale　features　in　the　image,　resulting　in　poor　generalization　performance　and　instability　of　the　model.　The　disadvantages　are　mainly　reflected　in　the　color　distortion,　color　unsaturation　and　artifacts.　Current　methods　unable　to　adjust　the　exposure　effectively,　resulting　in　uneven　exposure　or　partial　overexposure.　To　address　these　issues,　we　propose　an　end-to-end　low-light　image　enhancement　model,　which　is　called　multiscale　low-light　image　enhancement　network　with　illumination　constraint　(MLLEN-IC),　to　achieve　preferable　generalization　ability　and　stable　performance.　On　the　one　hand,　we　use　the　squeeze-and-excitation-Res2Net　block　(SE-Res2block)　as　a　base　unit　to　enhance　the　model＇s　ability　by　extracting　deep　multiscale　features.　On　the　other　hand,　to　make　the　model　more　adaptable　in　low-light　image　enhancement　tasks,　we　calculate　the　illumination　constraint　by　the　low-light　itself　to　prevent　overexposure,　uneven　exposure,　and　unsaturated　colors.　Extensive　experiments　are　conducted　to　demonstrate　MLLEN-IC　not　only　adjusts　light　levels,　but　also　has　a　more　natural　visual　effect,　and　avoids　problems　such　as　color　distortion,　artifacts,　and　uneven　exposure.　In　particular,　MLLEN-IC　has　pretty　generalization　and　stability　performance.　The　source　code　and　supplementary　are　available　at　https://github.com/CCECfgd/MLLEN-IC.　©　1991-2012　IEEE.