Aiming　to　solve　the　low　illumination　images　of　low　brightness,　low　contrast,　and　blurred　edges,　an　NSCT　(Non-Subsampled　Contourlet　Transform)　domain　images　enhancement　algorithm　is　proposed　based　on　adaptive　Retinex　and　adaptive　fractional　differential.　Firstly,　multi-scale　NSCT　decomposition　of　low-illuminance　images　is　needed　to　obtain　low-frequency　subbed　images　and　high-frequency　subbed　coefficients.　The　image　brightness　of　low-frequency　images　needs　to　be　improved　by　multi-scale　Retinex,　and　using　non-linear　bilateral　filtering　function　to　estimate　the　illumination　component.　The　illuminance　component　is　corrected　with　the　gamma　correction　functions　to　improve　the　dynamic　range　of　the　image,　and　the　reflection　component　is　corrected　using　the　influence　factor　to　enrich　its　hierarchy　and　enhance　the　overall　outline　of　the　image.　Then,　the　Bayes　threshold　is　used　to　isolate　noise　in　the　high-frequency　part,　the　adaptive　fractional　differentiation　can　be　used　to　enhance　the　details　of　the　edges　and　textures　of　the　image.　Finally,　NSCT　reconstructs　the　processed　image.　The　experimental　results　show　that　the　contrast,　sharpness　and　information　entropy　of　the　algorithm　in　this　paper　are　improved　by　10.7%,　9.8%,　and　2.3%　on　average　compared　with　the　existing　enhancement　methods.　The　enhanced　image　is　also　superior　to　existing　algorithms　in　terms　of　details　and　edge　preservation,　which　improves　the　overall　visual　effect　of　the　image.　©　2020,　Science　Press.　All　rights　reserved.