Recently,　techniques　based　on　dictionary　learning　for　sparse　representation　have　demonstrated　promising　results　for　depth　or　disparity　maps　restoration.　However,　we　show　that　these　methods　are　not　robust　due　to　the　fact　that　depth　or　disparity　maps　are　not　only　slightly　contaminated　by　additive　Gaussian　noise　but　also　seriously　corrupted　with　outliers,　occlusions,　or　even　variable　uncertainties.　These　seriously　corrupted　pixels　not　only　lead　to　irregular　structures　obtained　by　dictionary　but　also　seriously　deteriorate　the　sparse　coding　effectiveness.　To　overcome　these　problems,　in　this　paper　we　propose　a　new　robust　sparse　representation　framework　to　restore　depth　maps.　In　our　proposed　framework,　seriously　corrupted　pixels　can　be　automatically　identified　and　their　disturbance　effects　are　gradually　diminished　through　a　few　iterations.　Thus,　our　proposed　framework　is　more　robust　for　depth　restoration.　Experimental　results　are　presented　to　demonstrate　the　effectiveness　of　the　proposed　framework.