Low-light　image　enhancement　(LLIE)　is　a　challenging　task,　due　to　the　multiple　degradation　problems　involved,　such　as　low　brightness,　color　distortion,　heavy　noise,　and　detail　degradation.　Existing　deep　learning-based　LLIE　methods　mainly　use　encoder–decoder　networks　or　full-resolution　networks,　which　excel　at　extracting　context　or　detail　information,　respectively.　Since　detail　and　context　information　are　both　required　for　LLIE,　existing　methods　cannot　solve　all　the　degradation　problems.　To　solve　the　above　problem,　we　propose　an　LLIE　method　based　on　collaboratively　enhanced　and　integrated　detail-context　information　(CoEIDC).　Specifically,　we　propose　a　full-resolution　network　with　two　collaborative　subnetworks,　namely　the　detail　extraction　and　enhancement　subnetwork　(DE2-Net)　and　context　extraction　and　enhancement　subnetwork　(CE2-Net).　CE2-Net　extracts　context　information　from　the　features　of　DE2-Net　at　different　stages　through　large　receptive　field　convolutions.　Moreover,　a　collaborative　attention　module　(CAM)　and　a　detail-context　integration　module　are　proposed　to　enhance　and　integrate　detail　and　context　information.　CAM　is　reused　to　enhance　the　detail　features　from　multi-receptive　fields　and　the　context　features　from　multiple　stages.　Extensive　experimental　results　demonstrate　that　our　method　outperforms　the　state-of-the-art　LLIE　methods,　and　is　applicable　to　other　image　enhancement　tasks,　such　as　underwater　image　enhancement.　©　2025　Elsevier　Ltd