Due　to　light　scatter　and　absorption　in　waterbody,　underwater　imaging　can　be　easily　impaired　with　low　contrast　and　visual　distortion.　The　resulting　images　are　often　unable　to　meet　the　quality　requirements　of　human　perception　and　computer　processing.　Therefore,　Underwater　Image　Enhancement　(UIE)　has　been　attracting　extensive　research　efforts.　Although　deep　learning　has　demonstrated　its　great　success　in　many　vision　tasks,　its　huge　amounts　of　parameters　and　computations　are　not　conducive　to　UIE　in　resource-limited　scenarios.　In　this　paper,　we　address　this　issue　by　proposing　a　Lightweight　Cascaded　Network　(LCNet)　based　on　Laplacian　image　pyramids.　At　each　pyramid　level,　we　implement　cascaded　blocks　upon　a　residual　network.　Specifically,　high　quality　residuals　can　be　progressively　predicted　with　significantly　reduced　complexity　in　a　coarse-to-fine　fashion.　Furthermore,　these　sub-networks　are　recursively　nested　to　build　our　LCNet,　thereby　reducing　the　overall　computational　complexity　with　reused　parameters.　Extensive　experiments　demonstrate　that　the　proposed　method　performs　favorably　against　the　state-of-the-arts　in　terms　of　visual　quality,　model　parameters　and　complexity.