The　curve-based　lighting　adjustment　technique　is　widely　used　in　fields　such　as　photography　and　image　processing.　The　deep　learning-based　lighting　curve　adjustment　method　has　shown　excellent　performance　in　the　field　of　low-light　image　enhancement.　However,　existing　curve-based　deep　learning　methods　tend　to　use　complex　mathematical　formulas　to　define　the　curve　model　and　add　a　large　number　of　regularization　constraints　to　ensure　that　the　curve　conforms　to　real　physical　scenes.　This　limits　the　flexibility　of　the　lighting　curve,　making　it　unable　to　accurately　enhance　brightness　for　low-light　images,　resulting　in　problems　such　as　regional　color　distortion　and　overall　color　bias.　To　solve　this　problem,　we　propose　a　novel　low-light　image　enhancement　model　called　Discrete　Brightness　Curve　Estimation　(DBCE-Net).　In　DBCE-Net,　we　introduce　a　new　method　for　defining　curves　to　enhance　regional　illumination　more　effectively.　At　the　same　time,　we　propose　a　discrete　parameter　calculation　network　based　on　mutual　attention　mechanism　to　estimate　the　discrete　brightness　adjustment　curve　from　low-light　images.　Finally,　we　use　a　multi-scale　denoising　network　to　handle　noise　introduced　by　brightness　enhancement　in　shadow　areas.　Extensive　experiments　on　various　datasets　have　demonstrated　that　our　DBCE-Net　achieves　competitive　performance　in　terms　of　objective　quantitative　metrics　and　subjective　visual　quality　evaluation.　©　2024　SPIE.