With　the　unavailability　of　scene　depth　information,　single　-sensor　dehazing　methods　based　on　deep　learning　or　prior　information　do　not　effectively　work　in　dense　foggy　scenes.　An　effective　approach　is　to　remove　the　dense　fog　by　fusing　visible　and　near　-infrared　images.　However,　the　current　dehazing　algorithms　based　on　near　-infrared　and　visible　images　experience　color　distortion　and　information　loss.　To　overcome　these　challenges,　we　proposed　a　color　-preserving　dehazing　method　that　fuses　near　-infrared　and　visible　images　by　introducing　a　dataset　(VN-Haze)　of　visible　and　near　-infrared　images　captured　under　hazy　conditions.　A　twostage　image　enhancement　(TSE)　method　that　can　effectively　rectify　the　color　of　visible　images　affected　by　fog　was　proposed　to　prevent　the　introduction　of　distorted　color　information.　Furthermore,　we　proposed　an　adaptive　luminance　mapping　(ALM)　method　to　prevent　color　bias　in　fusion　images　caused　by　excessive　differences　in　brightness　between　visible　and　near　-infrared　images　that　occur　in　vegetation　areas.　The　proposed　visiblepriority　fusion　strategy　reasonably　allocates　weights　for　visible　and　near　-infrared　images,　minimizing　the　loss　of　important　features　in　visible　images.　Compared　with　existing　dehazing　algorithms,　the　proposed　algorithm　generates　images　with　natural　colors　and　less　distortion　and　retains　important　visible　information.　Moreover,　it　demonstrates　remarkable　performance　in　objective　evaluations.