Images　captured　in　fog　are　often　affected　by　scattering.　Due　to　the　absorption　and　scattering　of　light　by　aerosols　and　water　droplets,　the　image　quality　will　be　seriously　degraded.　The　specific　manifests　are　brightness　decrease,　contrast　decrease,　image　blur,　and　noise　increase.　In　the　single-image　dehazing　method,　the　image　degradation　model　is　essential.　In　this　paper,　an　effective　image　degradation　model　is　proposed,　in　which　the　hierarchical　deconvolution　strategy　based　on　transmission　map　segmentation　can　effectively　improve　the　accuracy　of　image　restoration.　Specifically,　the　transmission　map　is　obtained　by　using　the　dark　channel　prior　(DCP)　method,　then　the　transmission　histogram　is　fitted.　The　next　step　is　to　divide　the　image　region　according　to　the　fitting　results.　Furthermore,　to　more　accurately　recover　images　of　complex　objects　with　a　large　depth　of　field,　different　levels　of　inverse　convolution　are　adopted　for　different　regions.　Finally,　the　sub-images　of　different　regions　are　fused　to　get　the　dehazing　image.　We　tested　the　proposed　method　using　synthetic　fog　images　and　natural　fog　images　respectively.　The　proposed　method　is　compared　with　eight　advanced　image　dehazing　methods　on　quantitative　rating　indexes　such　as　peak　signal-to-noise　ratio　(PSNR),　structural　similarity　(SSIM),　image　entropy,　natural　image　quality　evaluator　(NIQE),　and　blind/referenceless　image　spatial　quality　evaluator　(BRISQUE).　Both　subjective　and　objective　evaluations　show　that　the　proposed　method　achieves　competitive　results.　©　2023　Optica　Publishing　Group　under　the　terms　of　the　Optica　Open　Access　Publishing　Agreement.