Low-light　image　enhancement　(LLIE)　aims　to　recover　high-quality　images　from　low-quality　images　acquired　in　dimly　illuminated　scenes.　However,　deep　learning　algorithms　often　struggle　with　uneven　exposure　and　obscured　texture　features.　To　address　these　obstacles,　we　propose　a　simple　but　novel　Transformer　structure　for　LLIE,　called　Double　Collapse　Transformer　(DCT　Former),　which　has　the　advantage　of　modeling　non-local　self-attention　and　capturing　long-range　dependencies　easily.　The　core　of　DCT　Former　is　multiple　CT　blocks　composed　of　pixel-level　spaces　and　channel　self-attention,　which　can　effectively　aggregate　features　in　both　the　space　and　channel　dimensions.　Furthermore,　we　design　the　Local　Processing　Unit　(LPU)　and　an　Inverted　Residual　Feed-Forward　Module　(IRFFN)　to　further　enhance　the　model＇s　ability　to　learn　effective　features　from　current　local　information.　DCT　Former　adopts　a　high-resolution　preservation　mechanism　overall　and　gradually　integrates　deep　features　to　achieve　intra-block　feature　aggregation.　Experimental　results　on　existing　benchmark　datasets　demonstrate　the　superiority　of　the　proposed　DCT　Former.　©　2024　John　Wiley　and　Sons　Inc.　All　rights　reserved.