Photos　captured　under　low-light　conditions　suffer　from　multiple　coupling　problems,　i.e.,　low　brightness,　color　distortion,　heavy　noise,　and　detail　degradation,　making　low-light　image　enhancement　a　challenging　task.　Existing　deep　learning-based　low-light　image　enhancement　methods　typically　focus　on　improving　the　illumination　and　color　while　neglecting　the　noise　in　the　enhanced　image.　To　solve　the　above　problems,　this　paper　proposes　a　low-light　image　enhancement　method　based　on　task　decoupling.　According　to　the　different　requirements　for　high-level　and　low-level　features,　the　low-light　image　enhancement　task　is　decoupled　into　two　subtasks:　illumination　and　color　enhancement　and　detail　reconstruction.　Based　on　the　task　decoupling,　we　propose　a　two-branch　low-light　image　enhancement　network　(TBLIEN).　The　illumination　and　color　enhancement　branch　is　built　as　a　U-Net　structure　with　global　feature　extraction,　which　exploits　deep　semantic　information　for　illumination　and　color　improvement.　The　detail　reconstruction　branch　uses　a　fully　convolutional　network　that　preserves　the　original　resolution　while　performing　detail　restoration　and　noise　removal.　In　addition,　for　the　detail　reconstruction　branch,　we　design　a　half-dual　attention　residual　module.　Our　module　enhances　features　through　spatial　and　channel　attention　mechanisms　while　preserving　their　context,　allowing　precise　detail　reconstruction.　Extensive　experiments　on　real　and　synthetic　datasets　show　that　our　model　outperforms　other　state-of-the-art　methods,　and　has　better　generalization　capability.　Our　method　is　also　applicable　to　other　image　enhancement　tasks,　i.e.,　underwater　image　enhancement.　©　2024　Chinese　Institute　of　Electronics.　All　rights　reserved.