Super-Resolution: Reconstruction or Recognition?

Abstract

Super-resolution is usually posed as a reconstruction problem. The low resolution input images are assumed to be noisy, down-sampled versions of an unknown super-resolution image that is to be estimated. A common way of inverting the down-sampling process is to write down the reconstruction constraints and then solve them, often adding a smoothness prior to regularize the solution. In this paper, we present two results which both show that there is more to super-resolution than image reconstruction. We first analyze the reconstruction constraints and show that they provide less and less useful information as the magnification factor increases. Afterwards, we describe a "hallucination" algorithm, incorporating the recognition of local features in the low resolution images, which outperforms existing reconstruction-based algorithms.