Wavelength-dependent　light　absorption　and　scattering　will　reduce　the　quality　of　underwater　images.　Therefore,　the　characteristics　of　underwater　images　are　different　from　those　taken　in　natural.　Low-quality　underwater　images　affect　the　accuracy　of　pattern　recognition,　visual　understanding,　and　key　feature　extraction　in　underwater　scenes.　In　this　paper,　we　enhance　the　underwater　image　using　a　multi-scale　generative　adversarial　network　with　adjacent　scale　feature　addition.　Adjacent　scale　feature　addition　allows　the　network　to　more　effectively　capture　the　relevant　characteristics　between　two　image　domains.　The　multi-scale　discriminator　can　let　the　enhanced　image　more　closer　to　the　natural　image.　Our　method　does　not　rely　on　transmission　maps　and　atmospheric　light　estimation.　Experiments　on　a　large　amount　of　synthetic　data　and　real　data　show　that　our　method　is　superior　to　the　state-of-the-art　methods.　©　2021,　Springer　Nature　Singapore　Pte　Ltd.