Due　to　refraction,　absorption,　and　scattering　of　light　by　suspended　particles　in　water,　underwater　images　are　characterized　by　low　contrast,　blurred　details,　and　color　distortion.　In　this　paper,　a　fusion　algorithm　to　restore　and　enhance　underwater　images　is　proposed.　It　consists　of　a　color　restoration　module,　an　end-to-end　defogging　module　and　a　brightness　equalization　module.　In　the　color　restoration　module,　a　color　balance　algorithm　based　on　CIE　Lab　color　model　is　proposed　to　alleviate　the　effect　of　color　deviation　in　underwater　images.　In　the　end-to-end　defogging　module,　one　end　is　the　input　image　and　the　other　end　is　the　output　image.　A　CNN　network　is　proposed　to　connect　these　two　ends　and　to　improve　the　contrast　of　the　underwater　images.　In　the　CNN　network,　a　sub-network　is　used　to　reduce　the　depth　of　the　network　that　needs　to　be　designed　to　obtain　the　same　features.　Several　depth　separable　convolutions　are　used　to　reduce　the　amount　of　calculation　parameters　required　during　network　training.　The　basic　attention　module　is　introduced　to　highlight　some　important　areas　in　the　image.　In　order　to　improve　the　defogging　network＇s　ability　to　extract　overall　information,　a　cross-layer　connection　and　pooling　pyramid　module　are　added.　In　the　brightness　equalization　module,　a　contrast　limited　adaptive　histogram　equalization　method　is　used　to　coordinate　the　overall　brightness.　The　proposed　fusion　algorithm　for　underwater　image　restoration　and　enhancement　is　verified　by　experiments　and　comparison　with　previous　deep　learning　models　and　traditional　methods.　Comparison　results　show　that　the　color　correction　and　detail　enhancement　by　the　proposed　method　are　superior.