Image　dehazing　is　a　crucial　image　processing　step　for　outdoor　vision　systems.　However,　images　recovered　through　conventional　image　dehazing　methods　that　use　either　haze-relevant　priors　or　heuristic　cues　to　estimate　transmission　maps　may　not　lead　to　sufficiently　accurate　haze　removal　from　single　images.　The　most　commonly　observed　effects　are　darkened　and　brightened　artifacts　on　some　areas　of　the　recovered　images,　which　cause　considerable　loss　of　fidelity,　brightness,　and　sharpness.　This　paper　develops　a　variational　image　dehazing　method　on　the　basis　of　a　color-transfer　image　dehazing　model　that　is　superior　to　conventional　image　dehazing　methods.　By　creating　a　color-transfer　image　dehazing　model　to　remove　haze　obscuration　and　acquire　information　regarding　the　coefficients　of　the　model　by　using　the　devised　convolutional　neural　network-based　deep　framework　as　a　supervised　learning　strategy,　an　image　fidelity,　brightness,　and　sharpness　can　be　effectively　restored.　The　experimental　results　verify　through　quantitative　and　qualitative　evaluations　of　either　synthesized　or　real　haze　images,　and　the　proposed　method　outperforms　existing　single　image　dehazing　methods.