Recent　advancements　in　low-light　image　enhancement　(LLIE)　through　deep　learning　have　been　significant.　However,　these　methods　often　struggle　with　effectively　handling　noise　in　dark　regions,　hindering　the　recovery　of　high-quality　texture　details　obscured　by　noise.　Moreover,　existing　approaches　typically　treat　the　entire　image　as　a　uniform　entity,　neglecting　the　semantic　nuances　of　distinct　regions.　This　oversight　may　cause　a　network　to　deviate　from　an　area＇s　original　color　without　semantic　priors.　In　response　to　these　challenges,　we　introduce　the　Semantic-Guided　Denoising　Diffusion　Probabilistic　Model　(SG-DDPM).　Leveraging　a　diffusion　model　for　LLIE,　our　approach　employs　a　sequence　of　denoising　refinement　processes　to　restore　realistic　details　in　dark　areas.　We　have　optimized　the　inference　process　of　the　diffusion　model　to　enhance　its　speed.　Additionally,　we　integrate　the　Segment　Anything　Model　(SAM)　to　extract　semantic　information　from　low-light　images,　guiding　the　diffusion　model　through　conditional　guidance.　Experimental　results　demonstrate　that　SG-DDPM　exhibits　competitive　performance　across　three　image　enhancement　datasets,　showcasing　improvements　in　quantitative　metrics　and　visual　quality.　©　2024　IEEE.