  Image hallucination and super-resolution have been studied for decades, and
many approaches have been proposed to upsample low-resolution images using
information from the images themselves, multiple example images, or large image
databases. However, most of this work has focused exclusively on small
magnification levels because the algorithms simply sharpen the blurry edges in
the upsampled images - no actual new detail is typically reconstructed in the
final result. In this paper, we present a patch-based algorithm for image
hallucination which, for the first time, properly synthesizes novel high
frequency detail. To do this, we pose the synthesis problem as a patch-based
optimization which inserts coherent, high-frequency detail from
contextually-similar images of the same physical scene/subject provided from
either a personal image collection or a large online database. The resulting
image is visually plausible and contains coherent high frequency information.
We demonstrate the robustness of our algorithm by testing it on a large number
of images and show that its performance is considerably superior to all
state-of-the-art approaches, a result that is verified to be statistically
significant through a randomized user study.

Computer Vision and Pattern Recognition;Multimedia