Dust　degrades　image　content　and　causes　image　color　cast,　which　negatively　impacts　on　many　high-level　computer　vision　tasks.　In　this　paper,　we　proposed　a　dedusting　network　with　color　cast　correction　for　a　single　dusty　image　(SIDNet).　The　SIDNet　contains　several　dust-aware　representation　extraction　(DustAre)　modules　with　the　same　structure.　Each　DustAre　module　contains　two　branches.　The　first　branch　encodes　the　input　to　estimate　global　veiling-light　and　local　spatial　information.　The　second　branch　generates　a　dust-aware　map　and　fuses　the　global　veiling-light,　the　local　spatial　information　and　the　dust-aware　map　to　generate　the　output.　To　further　improve　real　dusty　image　dedusting　performance,　the　SIDNet　introduces　a　color　cast　correction　scheme　to　our　neural　network.　After　considering　that　the　average　chromaticity　values　of　a　dusty　image　in　CIELAB　color　space　are　usually　larger　than　those　of　a　clean　(dust-free)　image,　the　SIDNet　defines　a　new　loss　function　to　better　guide　the　network　training.　Additionally,　we　also　construct　a　new　synthetic　dusty　image　dataset　for　network　training,　which　additionally　considers　the　scene　depth　relationship　between　real　dusty　image　and　dust-free　image.　Experiments　on　synthetic　and　real　dusty　images　show　that　the　SIDNet　achieves　better　dedusting　performance　compared　to　state-of-theart　image　restoration　methods.