Enhancing　the　quality　of　underwater　images　is　crucial　for　advancements　in　the　fields　of　underwater　exploration　and　underwater　rescue.　Existing　underwater　image　enhancement　methods　typically　rely　on　paired　underwater　images　and　reference　images　for　training.　However,　obtaining　corresponding　reference　images　for　underwater　images　is　challenging　in　practice.　In　contrast,　acquiring　high-quality　unpaired　underwater　images　or　images　captured　on　land　are　relatively　more　straightforward.　Furthermore,　existing　techniques　for　underwater　image　enhancement　often　struggle　to　address　a　variety　of　distortion　types　simultaneously.　To　avoid　the　reliance　on　paired　training　data,　reduce　the　difficulty　of　acquiring　training　data,　and　effectively　handle　diverse　types　of　underwater　image　distortions,　in　this　paper,　we　propose　a　novel　unpaired　underwater　image　enhancement　method　based　on　the　frequency-decomposed　generative　adversarial　network　(FD-GAN).　We　design　a　dual-branch　generator　based　on　high　and　low　frequencies　to　reconstruct　high-quality　underwater　images.　Specifically,　feature-level　wavelet　transform　is　introduced　to　separate　the　features　into　low-frequency　and　high-frequency　parts.　Then　the　separated　features　are　processed　by　a　cycle-consistent　generative　adversarial　network,　so　as　to　simultaneously　enhance　the　color　and　luminance　in　the　low-frequency　component　and　details　in　the　high-frequency　part.　More　specific,　the　low-frequency　branch　employs　an　encoder-decoder　structure　with　a　low-frequency　attention　mechanism　to　enhance　the　color　and　brightness　of　the　image.　The　high-frequency　branch　utilizes　parallel　high-frequency　attention　mechanisms　to　enhance　various　high-frequency　components,　thereby　achieving　the　restoration　of　image　details.　Experimental　results　on　multiple　datasets　show　that　the　proposed　method　trained　with　unpaired　high-quality　underwater　images　or　unpaired　high-quality　underwater　images　and　on-land　images,　can　effectively　generate　high-quality　underwater　enhanced　images　and　the　proposed　method　is　superior　to　the　state-of-the-art　underwater　image　enhancement　methods　in　terms　of　effectiveness　and　generalization.　©　2025　Chinese　Institute　of　Electronics.　All　rights　reserved.