Inpainting with sparse linear combinations of exemplars

We introduce a new exemplar-based inpainting algorithm that represents the region to be inpainted as a sparse linear combination of example blocks, extracted from the image being inpainted or an external training image set. This method is conceptually simple, being computed by minimization of a simple functional, and avoids the complexity of correctly ordering the filling in of missing regions of other exemplar-based methods. Initial performance comparisons on small inpainting regions indicate that this method provides similar or better performance than other recent methods.