Haze　removal　from　images　is　a　fundamental　computer　vision　challenge　with　significant　implications　for　various　applications.　Currently,　there　are　numerous　growing　demands　for　4K　image　processing　methods　on　the　mobile　and　IOT　(Internet　of　Things)　devices.　Absence　of　large-scale　4K　benchmark　datasets　hampers　progress　in　this　area,　especially　for　the　task　of　dehazing.　The　challenges　of　building　an　ultra-high　definition　(UHD)　dehazed　dataset　are　the　lack　of　estimation　methods　for　UHD　depth　maps　and　the　migration　strategy　from　synthetic　to　real　domains　for　UHD　with　haze　images　in　constrained　computational　resources.　To　address　these　problems,　we　develop　a　novel　synthetic　method　to　simulate　4K　hazy　images　(including　nighttime　and　daytime　scenes)　from　clear　images,　which　first　estimates　the　scene　depth,　simulates　the　light　rays　and　object　reflectance,　then　migrates　the　synthetic　images　to　real　domains　by　using　a　GAN,　and　finally　yeilds　the　hazy　effects　on　4K　resolution　images.　We　wrap　these　synthesized　images　into　a　benchmark　called　the　4K-HAZE　dataset.　Specifically,　we　design　the　CS-Mixer　(an　MLP-based　model　that　integrates　Channel　domain　and　Spatial　domain)　to　estimate　the　depth　map　of　4K　clear　images,　the　GU-Net　to　migrate　a　4K　synthetic　image　to　the　real　hazy　domain.　The　most　appealing　aspect　of　our　approach　is　the　capability　to　run　a　4K　image　on　a　single　GPU　with　24G　RAM　in　real-time　(33fps).　Additionally,　this　work　presents　an　objective　assessment　of　several　state-of-the-art　single　image　dehazing　methods　that　were　evaluated　using　4K-HAZE　dataset.　At　the　end　of　the　paper,　we　discuss　the　limitations　of　the　4K-HAZE　dataset　and　its　social　implications.　©　2025　Elsevier　B.V.