Repairing Sparse Low-Rank Texture

In this paper, we show how to harness both lowrank and sparse structures in regular or near regular textures for image completion. Our method leverages the new convex optimization for low-rank and sparse signal recovery and can automatically correctly repair the global structure of a corrupted texture, even without precise information about the regions to be completed. Through extensive simulations, we show our method can complete and repair textures corrupted by errors with both random and contiguous supports better than existing low-rank matrix recovery methods. Through experimental comparisons with existing image completion systems (such as Photoshop) our method demonstrate significant advantage over local patch based texture synthesis techniques in dealing with large corruption, non-uniform texture, and large perspective deformation.