Inpainting by Joint Optimization of Linear Combinations of Exemplars

Exemplar-based methods, in which actual image blocks are used to fill in missing content, have achieved state of the art performance in image inpainting. The majority of these adopt a progressive approach, filling in the missing region inwards from the boundary. The final result is highly dependent on fill order, and while significant progress has been made on the choice of this order, the greedy nature of such a process leads to artifacts in some cases. The alternative exemplar-based approach proposed here is defined via joint optimization of a single functional, simultaneously assigning an estimated value to the entire inpainting region. The results are found to be highly competitive with other recent inpainting methods.