Image　dehazing　is　a　crucial　preprocessing　step　in　computer　vision　for　enhancing　image　quality　and　enabling　many　downstream　applications.　However,　existing　methods　often　do　not　accurately　restore　hazy　images　while　maintaining　computational　efficiency.　To　overcome　this　challenge,　we　propose　ERCO-Net　a　new　fusion　framework　that　combines　edge　restriction　and　contextual　optimization　methods.　By　using　boundary　constraints,　ERCO-Net　extend　the　boundaries　that　help　in　protecting　the　edges　and　structures　of　an　image.　Contextual　optimization　impacts　the　final　quality　of　the　dehazed　image　by　enhancing　smoothness　and　coherence.　We　compare　ERCO-Net　with　conventional　approaches　such　as　dark　channel　prior　(DCP),　All-　in-one　dehazing　network　(AoD),　and　Feature　fusion　attention　network　(FFA-Net).　The　comparative　evaluation　highlights　the　effectiveness　of　the　proposed　fusion　method,　providing　significant　improvement　in　image　clarity,　contrast,　and　colors.　The　combination　of　edge　restriction　and　contextual　optimization　not　only　enhances　the　quality　of　dehazing　but　also　decreases　computational　complexity,　presenting　a　promising　avenue　for　advancing　image　restoration　techniques.　The　source　code　is　available　at　https://github.com/FatimaAyub12/Image-Dehazing-.