It　is　a　challenging　task　to　obtain　high-quality　images　in　low-light　scenarios.　While　existing　low-light　image　enhancement　methods　learn　the　mapping　from　low-light　to　clear　images,　such　a　straightforward　approach　lacks　the　targeted　design　for　real-world　scenarios,　hampering　their　practical　utility.　As　a　result,　issues　such　as　overexposure　and　color　distortion　are　likely　to　arise　when　processing　images　in　uneven　luminance　or　extreme　darkness.　To　address　these　issues,　we　propose　an　adaptive　luminance　enhancement　and　high-fidelity　color　correction　network　(LCNet),　which　adopts　a　strategy　of　enhancing　luminance　first　and　then　correcting　color.　Specifically,　in　the　adaptive　luminance　enhancement　stage,　we　design　a　multi-stage　dual　attention　residual　module　(MDARM),　which　incorporates　parallel　spatial　and　channel　attention　mechanisms　within　residual　blocks.　This　module　extracts　luminance　prior　from　the　low-light　image　to　adaptively　enhance　luminance,　while　suppressing　overexposure　in　areas　with　sufficient　luminance.　In　the　high-fidelity　color　correction　stage,　we　design　a　progressive　multi-scale　feature　fusion　module　(PMFFM)　that　combines　progressively　stage-wise　multi-scale　feature　fusion　with　long/short　skip　connections,　enabling　thorough　interaction　between　features　at　different　scales　across　stages.　This　module　extracts　and　fuses　color　features　with　varying　receptive　fields　to　ensure　accurate　and　consistent　color　correction.　Furthermore,　we　introduce　a　multi-color-space　loss　to　effectively　constrain　the　color　correction.　These　two　stages　together　produce　high-quality　images　with　appropriate　luminance　and　high-fidelity　color.　Extensive　experiments　on　both　low-level　and　high-level　tasks　demonstrate　that　our　LCNet　outperforms　state-of-the-art　methods　and　achieves　superior　performance　for　low-light　image　enhancement　in　real-world　scenarios.　©　2015　IEEE.