The　task　of　low-light　image　enhancement　aims　to　reconstruct　details　and　visual　information　from　degraded　low-light　images.　However,　existing　deep　learning　methods　for　feature　processing　usually　lack　feature　differentiation　or　fail　to　implement　reasonable　differentiation　handling,　which　can　limit　the　quality　of　the　enhanced　images,　leading　to　issues　like　color　distortion　and　blurred　details.　To　address　these　limitations,　we　propose　Dual-Range　Information　Guidance　Network　(DRIGNet).　Specifically,　we　develop　an　efficient　U-shaped　architecture　Dual-Range　Information　Guided　Framework　(DGF).　DGF　decouples　traditional　image　features　into　dual-range　information　while　integrating　stage-specific　feature　properties　with　the　proposed　dual-range　information.　We　design　the　Global　Dynamic　Enhancement　Module　(GDEM)　using　channel　interaction　and　the　Detail　Focus　Module　(DFM)　with　three-directional　filter,　both　embedded　in　DGF　to　model　long-range　and　short-range　features　respectively.　Additionally,　we　introduce　a　feature　fusion　strategy,　Attention-Guided　Fusion　Module　(AGFM),　which　merges　dual-range　information,　facilitating　complementary　enhancement.　In　the　encoder,　DRIGNet　extracts　coherent　long-range　information　and　enhances　the　global　structure　of　the　image;　in　the　decoder,　DRIGNet　captures　short-range　information　and　fuse　dual-rage　information　to　restore　detailed　areas.　Finally,　we　conduct　extensive　quantitative　and　qualitative　experiments　to　demonstrate　that　the　proposed　DRIGNet　outperforms　the　current　State-of-the-Art　(SOTA)　methods　across　ten　datasets.　©　2025　Elsevier　B.V.