When　shooting　street　scenes　at　night,　the　captured　images　may　be　underexposed　or　overexposed,　which　seriously　affects　human　visual　perception.　Therefore,　exposure　correction　is　required　for　these　images.　Most　existing　exposure　correction　methods　rely　heavily　on　reference　images　and　the　exposure　correction　results　are　not　thorough.　To　address　these　issues,　we　propose　a　zero-shot　multi-exposure　correction　method　based　on　S-curves,　called　ZSDECNet.　Our　method　is　divided　into　two　parts:　multi-exposure　correction　and　fusion.　First,　the　illumination　channel　of　the　image　and　the　corresponding　inverted　image　are　subjected　to　an　initial　exposure　correction.　Moreover,　by　exposure　fusion　technique,　we　select　the　best　exposed　area　for　exposure　fusion　from　the　exposure　correction　results　of　the　input　image,　the　illumination　channel,　and　its　inverted　channel　in　order　to　obtain　a　visually　optimal　exposure-corrected　image.　The　method　is　a　zero-shot　end-to-end　training　approach　that　does　not　require　additional　data.　In　addition,　the　S-type　exposure　correction　curve　corrects　both　underexposed　and　overexposed　areas,　which　makes　it　possible　to　obtain　more　thorough　exposure　correction　results.　Experiments　on　existing　datasets　with　various　exposure　conditions　(underexposed　and　overexposed)　and　on　a　real　nighttime　street　scene　dataset　show　that　our　method　outperforms　state-of-the-art　methods.　©　2025　Elsevier　B.V.