Currently,　mobile　laser　scanning　(MLS)　systems　can　conveniently　and　rapidly　measure　the　backscattered　laser　beam　properties　of　the　object　surfaces　in　large-scale　roadway　scenes.　Such　properties　is　digitalized　as　the　in-tensity　value　stored　in　the　acquired　point　cloud　data,　and　the　intensity　as　an　important　information　source　has　been　widely　used　in　a　variety　of　applications,　including　road　marking　inventory,　manhole　cover　detection,　and　pavement　inspection.　However,　the　collected　intensity　is　often　deviated　from　the　object　reflectance　due　to　two　main　factors,　i.e.　different　scanning　distances　and　worn-out　surfaces.　Therefore,　in　this　paper,　we　present　a　new　intensity-enhanced　method　to　gradually　and　efficiently　achieve　the　intensity　enhancement　in　the　MLS　point　clouds.　Concretely,　to　eliminate　the　intensity　inconsistency　caused　by　different　scanning　distances,　the　direct　relationship　between　scanning　distance　and　intensity　value　is　modeled　to　correct　the　inconsistent　intensity.　To　handle　the　low　contrast　between　3D　points　with　different　intensities,　we　proposed　to　introduce　and　adapt　the　dark　channel　prior　for　adaptively　transforming　the　intensity　information　in　point　cloud　scenes.　To　remove　the　isolated　intensity　noises,　multiple　filters　are　integrated　to　achieve　the　denoising　in　the　regions　with　different　point　densities.　The　evaluations　of　our　proposed　method　are　conducted　on　four　MLS　datasets,　which　are　acquired　at　different　road　scenarios　with　different　MLS　systems.　Extensive　experiments　and　discussions　demonstrate　that　the　proposed　method　can　exhibit　the　remarkable　performance　on　enhancing　the　intensities　in　MLS　point　clouds.