A Sampled Texture Prior for Image Super-Resolution

Super-resolution aims to produce a high-resolution image from a set of one or more low-resolution images by recovering or inventing plausible high-frequency image content. Typical approaches try to reconstruct a high-resolution image using the sub-pixel displacements of several lowresolution images, usually regularized by a generic smoothness prior over the high-resolution image space. Other methods use training data to learn low-to-high-resolution matches, and have been highly successful even in the single-input-image case. Here we present a domain-specific image prior in the form of a p.d.f. based upon sampled images, and show that for certain types of super-resolution problems, this sample-based prior gives a significant improvement over other common multiple-image super-resolution techniques.

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Lyndsey C. Pickup, Stephen J. Roberts, Andrew Zisserman
• Keywords: Common Multiple-image Super-resolution, Low-resolution Images, NIPS 2003, NIPS 2007, Sub-pixel Displacements