Underwater　image　enhancement　recovers　degraded　underwater　images　to　produce　corresponding　clear　images.　Image　enhancement　methods　based　on　deep　learning　usually　use　paired　data　to　train　the　model,　while　such　paired　data,　e.g.,　the　degraded　images　and　the　corresponding　clear　images,　are　difficult　to　capture　simultaneously　in　the　underwater　environment.　In　addition,　how　to　retain　the　detailed　information　well　in　the　enhanced　image　is　another　critical　problem.　To　solve　such　issues,　we　propose　a　novel　unpaired　underwater　image　enhancement　method　via　a　cycle　generative　adversarial　network　(UW-CycleGAN)　to　recover　the　degraded　underwater　images.　Our　proposed　UW-CycleGAN　model　includes　three　main　modules:　(1)　A　content　loss　regularizer　is　adopted　into　the　generator　in　CycleGAN,　which　constrains　the　detailed　information　existing　in　one　degraded　image　to　remain　in　the　corresponding　generated　clear　image;　(2)　A　blur-promoting　adversarial　loss　regularizer　is　introduced　into　the　discriminator　to　reduce　the　blur　and　noise　in　the　generated　clear　images;　(3)　We　add　the　DenseNet　block　to　the　generator　to　retain　more　information　of　each　feature　map　in　the　training　stage.　Finally,　experimental　results　on　two　unpaired　underwater　image　datasets　produced　satisfactory　performance　compared　to　the　state-of-the-art　image　enhancement　methods,　which　proves　the　effectiveness　of　the　proposed　model.