Robust　vision　restoration　of　underwater　images　remains　a　challenge.　Owing　to　the　lack　of　well-matched　underwater　and　in-air　images,　unsupervised　methods　based　on　the　cyclic　generative　adversarial　framework　have　been　widely　investigated　in　recent　years.　However,　when　using　an　end-to-end　unsupervised　approach　with　only　unpaired　image　data,　mode　collapse　could　occur,　and　the　color　correction　of　the　restored　images　is　usually　poor.　In　this　paper,　we　propose　a　data-　and　physics-driven　unsupervised　architecture　to　perform　underwater　image　restoration　from　unpaired　underwater　and　in-air　images.　For　effective　color　correction　and　quality　enhancement,　an　underwater　image　degeneration　model　must　be　explicitly　constructed　based　on　the　optically　unambiguous　physics　law.　Thus,　we　employ　the　Jaffe-McGlamery　degeneration　theory　to　design　a　generator　and　use　neural　networks　to　model　the　process　of　underwater　visual　degeneration.　Furthermore,　we　impose　physical　constraints　on　the　scene　depth　and　degeneration　factors　for　backscattering　estimation　to　avoid　the　vanishing　gradient　problem　during　the　training　of　the　hybrid　physical-neural　model.　Experimental　results　show　that　the　proposed　method　can　be　used　to　perform　high-quality　restoration　of　unconstrained　underwater　images　without　supervision.　On　multiple　benchmarks,　the　proposed　method　outperforms　several　state-of-the-art　supervised　and　unsupervised　approaches.　We　demonstrate　that　our　method　yields　encouraging　results　in　real-world　applications.