In　recent　years,　image　dehazing　tasks　have　primarily　focused　on　urban　or　indoor　scenarios,　with　little　attention　paid　to　the　study　of　image　dehazing　in　forest　fire　contexts.　The　degradation　of　images　caused　by　the　dense　smoke　generated　by　forest　fires　poses　a　significant　obstacle　to　firefighting　and　rescue　operations.　Unlike　ordinary　haze,　the　smoke　produced　by　forest　fires　is　characterized　by　its　non-homogeneous　density,　which　complicates　the　process　of　smoke　removal.　Moreover,　there　is　limited　research　dedicated　to　smoke　removal　specifically　in　wildfire　environment　images.　To　address　this　gap　in　image　dehazing　research　for　forest　fire　scenarios,　this　study　trains　and　tests　various　specialized　dehazing　methods　on　a　new　public　dataset　for　forest　fire　dehazing,　Fo-haze.　The　methods　are　divided　into　four　groups　based　on　their　network　structures　and　training　strategies.　Through　qualitative　and　quantitative　evaluations　of　the　models’　performance,　the　study　aims　to　assist　firefighters　in　assessing　the　dehazing　capabilities　of　different　models.　The　experimental　results　indicate　that　the　CHaIR　model　from　the　Technical　University　of　Munich,　Germany,　achieved　the　best　results,　with　a　PSNR　of　27.11　dB　and　an　SSIM　of　0.8721.　©　2025　Copyright　held　by　the　owner/author(s).