Low-light　image　enhancement　is　a　challenging　task　due　to　the　limited　visibility　in　dark　environments.　While　recent　advances　have　shown　progress　in　integrating　CNNs　and　Transformers,　the　inadequate　local-global　perceptual　interactions　still　impedes　their　application　in　complex　degradation　scenarios.　To　tackle　this　issue,　we　propose　BiFormer,　a　lightweight　framework　that　facilitates　local-global　collaborative　perception　via　bilateral　interaction.　Specifically,　our　framework　introduces　a　core　CNN-Transformer　collaborative　perception　block　(CPB)　that　combines　local-aware　convolutional　attention　(LCA)　and　global-aware　recursive　transformer　(GRT)　to　simultaneously　preserve　local　details　and　ensure　global　consistency.　To　promote　perceptual　interaction,　we　adopt　bilateral　interaction　strategy　for　both　local　and　global　perception,　which　involves　local-to-global　second-order　interaction　(SoI)　in　the　dual-domain,　as　well　as　a　mixed-channel　fusion　(MCF)　module　for　global-to-local　interaction.　The　MCF　is　also　a　highly　efficient　feature　fusion　module　tailored　for　degraded　features.　Extensive　experiments　conducted　on　low-level　and　high-level　tasks　demonstrate　that　BiFormer　achieves　state-of-the-art　performance.　Furthermore,　it　exhibits　a　significant　reduction　in　model　parameters　and　computational　cost　compared　to　existing　Transformer-based　low-light　image　enhancement　methods.　IEEE