Defogging　algorithms　based　on　atmospheric　model　always　had　atmospheric　light　and　medium　transmission　limited　by　statistical　or　hypothetical　information.　Hence　a　non-hypothetical　parameter　estimation　method　was　proposed.　For　precisely　acquiring　these　parameters,　the　atmospheric　light　was　solved　by　a　quad-tree　algorithm　firstly.　Secondly,　a　pre-trained　convolutional　neural　network　was　proposed　for　estimating　the　transmission　map　optimized　by　the　guided　filtering　algorithm　further.　Finally,　by　reversely　solving　the　atmospheric　scattering　model,　the　de-fogging　image　was　obtained.　Experiments　show　that　the　proposed　method　has　balanced　performance　on　each　index.　It　not　only　improves　the　degree　of　foggy　image　definition　and　brightness,　but　also　efficiently　avoids　the　Halo　effect.　Time　performance　also　analysis　indicates　that,　compared　to　other　defogging　algorithms,　efficiency　of　our　algorithm　using　CPU　has　increased　40　%　at　least.　After　parallelizing　the　time-consuming　guided　filtering　algorithm　through　CUDA,　the　efficiency　has　improved　remarkably　which　can　process　a　fog　image　with　the　resolution　of　640×480　pixels　only　in　0.048　9　s.　It　can　be　directly　applied　to　video　processing　to　meet　real-time　requirement.　©　2018,　Beijing　China　Science　Journal　Publishing　Co.　Ltd.　All　right　reserved.