Vision　systems　are　the　core　information　collection　module　in　outdoor　industrial　systems　such　as　factory　inspection　robots.　However,　haze　greatly　reduces　working　efficiency.　Existing　dehazing　methods　have　two　problems-first,　they　are　not　specifically　designed　for　the　industrial　systems;　second,　these　methods　include　several　assumptions　in　their　design　processes　and　imaging　models,　leading　to　unsatisfactory　results.　In　this　article,　an　approach　for　single　image　dehazing　is　proposed　to　improve　the　efficiency　of　outdoor　vision-based　systems.　First,　a　novel　haze　imaging　model　is　proposed　based　on　the　dichromatic　atmospheric　scattering　model.　It　considers　the　effects　of　multiple　scattering　and　involves　fewer　assumptions.　Then　a　data-driven　technique　called　sparse　representation　is　used　to　solve　this　model.　Considering　a　haze　image,　a　distorted　and　blurred　version　of　a　fine　image,　every　patch　is　presented　using　dedicatedly　prepared　over-complete　dictionaries　and　is　traced　back　to　a　haze-free　image.　Quantitative　and　qualitative　comparisons　on　a　number　of　real-world　haze　images　demonstrate　that　the　proposed　approach　not　only　is　more　stable　but　also　leads　to　better　dehazing　results.