To　improve　the　recovery　ability　of　polarization　dehazing　algorithms　in　fog　scenes，a　polarization　image　dehazing　algorithm　based　on　polarization　optimization　and　atmospheric　light　correction　is　proposed.　First，according　to　the　brightness　distribution　of　the　fog　scene，the　fog　image　was　decomposed　into　bright　residuals　and　dark　residuals　via　guided　filtering.　Second，to　optimize　the　degree　of　polarization，the　degrees　of　polarization　corresponding　to　the　bright　and　dark　residuals　were　increased　and　decreased，respectively.　This　optimized　degree　of　polarization　can　blur　the　atmospheric　light　image.　The　difference　value　of　the　degree　of　polarization　in　the　residuals　was　used　to　correct　the　atmospheric　light　for　ensuring　its　intensity　range　met　the　atmospheric　degradation　model.　Experiments　indicated　that　the　contrast　ratio　was　3.　07　times　that　in　original　hazy　images　after　dehazing　and　that　the　entropy　and　standard　deviation　of　dehazed　images　were　increased　by　9.　21%　and　61.　86%，respectively.　In　environments　with　different　concentrations　of　simulated　fog，the　proposed　algorithm　achieved　excellent　SSIM，CIEDE2000，and　PSNR　values.　Compared　with　the　state-of-art　dehazing　algorithms，the　effect　of　the　proposed　algorithm　was　obvious，and　it　recovered　the　scene　details　efficiently.　©　2023　Chinese　Academy　of　Sciences.　All　rights　reserved.