Deep　learning　computation　is　often　used　in　single-image　dehazing　techniques　for　outdoor　vision　systems.　Its　development　is　restricted　by　the　difficulties　in　providing　a　training　set　of　degraded　and　ground-truth　image　pairs.　In　this　paper,　we　develop　a　novel　model　that　utilizes　cycle　generative　adversarial　network　through　unsupervised　learning　to　effectively　remove　the　requirement　of　a　haze/depth　data　set.　Qualitative　and　quantitative　experiments　demonstrated　that　the　proposed　model　outperforms　existing　state-of-the-art　dehazing　models　when　tested　on　both　synthetic　and　real　haze　images.