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Image hallucination and super-resolution have been studied for decades, andmany approaches have been proposed to upsample low-resolution images usinginformation from the images themselves, multiple example images, or large imagedatabases. However, most of this work has focused exclusively on smallmagnification levels because the algorithms simply sharpen the blurry edges inthe upsampled images - no actual new detail is typically reconstructed in thefinal result. In this paper, we present a patch-based algorithm for imagehallucination which, for the first time, properly synthesizes novel highfrequency detail. To do this, we pose the synthesis problem as a patch-basedoptimization which inserts coherent, high-frequency detail fromcontextually-similar images of the same physical scene/subject provided fromeither a personal image collection or a large online database. The resultingimage is visually plausible and contains coherent high frequency information.We demonstrate the robustness of our algorithm by testing it on a large numberof images and show that its performance is considerably superior to allstate-of-the-art approaches, a result that is verified to be statisticallysignificant through a randomized user study.

Patch-Based Image Hallucination for Super Resolution with Detail Reconstruction from Similar Sample Images