Dust　is　a　common　air　pollution　source.　The　color　of　images　captured　under　dusty　weather　is　usually　yellow　even　brown,　which　reduces　scene　visibility　and　causes　the　loss　of　image　details.　To　remove　the　dust　and　make　image　scene　clear,　this　article　presents　a　simple　and　effective　image　dedusting　network　called　FFNet.　In　the　process　of　image　feature　extraction,　the　FFNet　uses　several　residual　blocks　with　smoothed　dilated　convolution,　that　is,　common　dilated　convolution　followed　by　separable　and　shared　(SS)　blockwise　fully　connected　operation　to　extend　the　receptive　field　and　reduce　gridding　artifacts　caused　by　common　dilated　convolution.　Furthermore,　the　FFNet　fuses　image　features　from　different　layers　via　an　adaptive　weighting　scheme.　Due　to　the　difficulty　of　collecting　real　dusty　images,　we　used　our　proposed　dusty　image　synthesis　scheme　to　achieve　data　augmentation　for　better　network　training.　Experiments　on　a　series　of　synthetic　and　real　dusty　images　demonstrate　that　the　FFNet　obtains　better　image　dedusting　performance　than　several　state-of-the-art　image　restoration　methods.