Although　image　enhancement　methods　have　been　widely　applied　in　various　outdoor　vision　systems,　the　existing　methods　still　face　two　critical　problems.　On　the　one　hand,　the　existing　methods　only　consider　a　single　degradation.　However,　in　practical　applications,　image　quality　is　usually　degraded　by　multiple　factors.　The　methods　designed　for　the　single　degradation　factor　cannot　achieve　good　performance　when　facing　multi-degraded　images.　On　the　other　hand,　the　imaging　model-based　enhancement　methods　which　use　prior　knowledge　or　handcrafted　features　to　perform　image　enhancement　may　bring　some　fitting　errors.　Therefore,　considering　multiple　degradations　in　images,　an　image　enhancement　method　is　proposed　in　this　paper.　Firstly,　a　new　image　degradation　model　based　on　the　multiple　scattering　model　is　proposed,　which　is　used　to　characterize　multiple　degradations　caused　by　haze,　mixed　with　blur　and　noise.　Then,　an　image　enhancement　convolutional　neural　network　(CNN)　based　on　ResNet　is　proposed　to　learn　the　implicit　mapping　model　between　low-quality　and　high-quality　images　in　the　pixel　domain　directly.　The　CNN　network　has　been　trained　with　an　end-to-end　learning　manner.　Experimental　results　on　the　synthetic　dataset　and　real-world　hazy　images　verify　the　superiority　of　the　proposed　method,　while　compared　with　the　state-of-the-art　methods.　©　2020,　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.