Low-light　images　are　generally　produced　by　shooting　in　a　low　light　environment　or　a　tricky　shooting　angle,　which　not　only　affect　people＇s　perception,　but　also　leads　to　the　bad　performance　of　some　artificial　intelligence　algorithms,　such　as　object　detection,　super-resolution,　and　so　on.　There　are　two　difficulties　in　the　low-light　enhancement　algorithm:　in　the　first　place,　applying　image　processing　algorithms　independently　to　each　low-light　image　often　leads　to　the　color　distortion;　the　second　is　the　need　to　restore　the　texture　of　the　extremely　low-light　area.　To　address　these　issues,　we　present　two　novel　and　general　approaches:　firstly,　we　propose　a　new　loss　function　to　constrain　the　ratio　between　the　corresponding　RGB　pixel　values　on　the　low-light　image　and　the　high-light　image;　secondly,　we　propose　a　new　framework　named　GLNet,　which　uses　the　dense　residual　connection　block　to　obtain　the　deep　features　of　the　low-light　images,　and　design　a　gray　scale　channel　network　branch　to　guide　the　texture　restoration　on　the　RGB　channels　by　enhancing　the　grayscale　image.　The　ablation　experiments　have　demonstrated　the　effectiveness　of　the　proposed　module　in　this　paper.　Extensive　quantitative　and　perceptual　experiments　show　that　our　approach　obtains　state-of-the-art　performance　on　the　public　dataset.　©　COPYRIGHT　SPIE.