Multi-scale decomposition based k-nearest-neighbor random search for fast image completion is presented. The image is decomposed using the bilateral filtering based down sampling. Starting from the coarsest level image, the most matching patch is searched using k-nearest-neighbor search algorithm based on the minimum heap for each coarse layer. The robust priority function is presented to determine the next patch that should be handled. The lower coarse layer is reconstructed using the bilateral filtering based up sampling after current coarse layer is repaired, so as to get the final result with iterative completion. Compared with related work, the presented algorithm preserves image details and edge information, and obtains higher completion quality. The completion results are evaluated utilizing the objective indictors. The experimental results show that presented method is effective, feasible, and the visual effect of the image completion is pleasing.
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