Inadequate　exposure　of　imaging　devices　in　low-light　environments　results　in　a　loss　of　image　information,　significantly　deteriorating　the　image　quality.　However,　current　low-light　image　enhancement　algorithms　commonly　suffer　from　issues　such　as　color　distortion　and　loss　of　fine　details　and　textures.　In　this　paper,　we　propose　a　frequency-guided　dual-collapse　Transformer　(FDCFormer)　network.　First,　in　response　to　color　distortion　after　enhancement,　we　propose　a　dual-collapse　Transformer　that　effectively　aggregates　features　from　both　spatial　and　channel　dimensions,　thus　capturing　global　information.　Subsequently,　because　relying　solely　on　enhancement　in　the　spatial　domain　often　makes　it　difficult　to　preserve　fine　details　and　textures,　we　design　multiple　mixed　residual　fast　Fourier　transform　blocks　as　additional　frequency　information　guidance　branches,　focusing　on　local　detail　information　at　the　image　edges.　Additionally,　we　employ　an　adaptive　dual-domain　information　fusion　module　that　combines　spatial　domain　and　frequency　domain　information　to　enrich　the　final　output　features.　Extensive　experiments　on　multiple　publicly　available　datasets　demonstrate　that　our　FDCFormer　outperforms　state-of-the-art　methods,　exceeding　Retinexformer　by　up　to　0.93　dB　on　average　across　five　paired　datasets.　We　also　employ　our　method　as　a　preprocessing　step　in　dark　detection,　our　method　improves　mean　average　precision　(mAP)　by　1.9%　over　the　baseline　model　on　ExDark　dataset,　revealing　the　latent　practical　values　of　our　method.　The　corresponding　codes　will　be　available　at　https://github.com/Fly175/FDCFormer.