Enhancing　underwater　images　is　pivotal　for　applications　like　underwater　exploration.　While　most　existing　methods　require　paired　underwater　and　reference　images　for　training,　acquiring　such　reference　images　is　difficult.　Current　techniques　often　suffer　from　information　loss,　resulting　in　blurred　details　and　lack　generalizability.　This　paper　introduces　an　unpaired　underwater　image　enhancement　method　using　the　Multi-Scale　Structural　and　Color　Enhanced　Generative　Adversarial　Network　(MSSCE-GAN).　We　also　introduce　a　detail　feature　recovery　module　and　an　attention　enhancement　module,　targeting　multiple　distortions　in　underwater　imagery.　Importantly,　our　method　can　produce　superior　enhanced　images　without　paired　training　data.　Experimental　evaluations　demonstrate　our　approach　outperforms　existing　techniques　in　both　effectiveness　and　generalizability　on　multiple　underwater　image　datasets.　©　2023　IEEE.