A Hardware-Accelerated Patch Search Engine for Image Completion

This paper proposes a GPU-accelerated patch search engine that efficiently Alls the unknown regions of an image caused by replacement or removal of part of the foreground. Previous approaches, such as inpainting and texture synthesis, are either fast, but not applicable for small-scale regions, or slow, but fills large regions with good quality. The algorithm in this paper is based on the patch-based best-fit searching strategy, in which a partly-known patch is filled by searching the known part of the image for a patch of pixels closely matching the known neighbors. This keeps the linear structure and texture of the image. Each patch is represented as a stream and processed in parallel in the GPU. We found that the exhaustive searching strategies used in previous work are the main cause of inefficiency; most matches are located in the neighborhood of the target patch. Inspired by this spatial continuity, we develop a very efficient search engine compared with previous work.