Enhancing　low-light　images　with　high　quality　is　a　highly　challenging　task　due　to　the　features　of　low-light　images　such　as　brightness,　color,　and　details　in　the　information　of　different　scales.　Existing　deep　learning-based　methods　fail　to　fully　utilize　multi-scale　features　and　fuse　multi-scale　features　to　comprehensively　enhance　the　brightness,　color　and　details　of　the　images.　To　address　these　problems,　a　Transformer-based　multi-scale　optimization　network　for　low-light　image　enhancement　is　proposed.　Firstly,　the　Transformer-based　multi-task　enhancement　module　is　designed.　Through　multi-task　training,　the　Transformer-based　enhancement　module　gains　the　ability　to　globally　model　brightness,　color,　and　details.　Therefore,　it　can　initially　cope　with　various　degradation　challenges　commonly　found　in　low-light　images,　such　as　insufficient　brightness,　color　deviation,　blurred　details　and　severe　noises.　Then,　the　architecture　combining　global　and　local　multi-scale　features　is　designed　to　progressively　optimize　the　features　at　different　scales.　Finally,　a　multi-scale　feature　fusion　module　and　an　adaptive　enhancement　module　are　proposed.　They　learn　and　fuse　the　information　association　among　different　scales,　while　adaptively　enhancing　images　in　various　local　multi-scale　branches.　Extensive　experiments　on　six　public　datasets,　including　paired　or　unpaired　images,　show　that　the　proposed　method　can　effectively　solve　the　problems　of　multiple　degradation　types,　such　as　brightness,　color,　details　and　noise　in　low-light　images.　©　2023　Journal　of　Pattern　Recognition　and　Artificial　Intelligence.　All　rights　reserved.