Underwater　imaging　is　usually　degraded　by　low　contrast　or　color　cast　due　to　light　absorption　and　scattering.　This　fact　limits　the　accuracy　of　object　recognition　in　underwater　and　marine　environments.　In　this　article,　to　address　this　issue,　we　propose　a　CURE-Net　that　progressively　improves　degraded　underwater　images　in　a　coarse-to-fine　way.　To　be　specific,　our　CURE-Net　consists　of　three　cascaded　subnetworks.　The　first　two　subnetworks　use　attention　and　gate　fusion　to　learn　multiscale　contextual　information,　whereas　the　third　subnetwork　preserves　fine　spatial　details.　A　detail　enhancement　block　and　a　supervised　recovery　block　are　deployed　between　subnetworks　to　further　improve　the　recovery　of　colors　and　details.　Our　proposed　CURE-Net　is　extensively　evaluated　on　popular　data　sets　and　achieves　state-of-the-art　performance.　Compared　with　its　peers,　our　method　is　also　more　friendly　to　object　recognition-underwater　objects　are　more　easily　recognized　after　being　processed　by　our　method.