Images　are　often　affected　by　insufficient　illumination　and　suffer　from　degradation　problems　such　as　low　brightness,　noise,　and　color　distortion,　which　results　in　reduced　image　quality.　Existing　low-light　image　enhancement　methods　based　on　Retinex　theory　decompose　images　into　reflectance　and　illumination　components,　which　are　adjusted　separately;　however,　the　intrinsic　connection　between　reflectance　and　illumination　during　decomposition　is　not　considered,　and　multi-scale　information　during　subsequent　adjustments　is　inadequately　utilized.　In　this　study,　we　propose　a　low-light　image　enhancement　network　based　on　Retinex　decomposition　and　multi-scale　adjustment　(RDMA),　which　performs　initial　decomposition　followed　by　subsequent　adjustment.　We　utilized　prior　knowledge　to　design　the　feature　interaction　module　(FIM)　and　the　feature　fusion　module　(FFM)　for　image　decomposition.　Furthermore,　a　coarse-to-fine　multi-scale　network　with　residual　channel　and　spatial　attention　(RCSA)　was　designed　to　remove　noise　from　reflectance,　suppress　color　distortion,　preserve　image　details,　and　adjust　the　brightness　of　illumination.　An　evaluation　of　various　low-light　image　datasets　and　comparisons　with　state-of-the-art　methods　showed　that　the　proposed　network　is　superior　in　terms　of　enhancement　results.