Home > Research > Publications & Outputs > Efficient learning-based image enhancement

Electronic data

Links

Text available via DOI:

View graph of relations

Efficient learning-based image enhancement: application to super-resolution and compression artifact removal

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Published
Close
Publication date2012
Host publicationProc. British Machine Vision Conference (BMVC) 2012
Pages14.1-14.12
Number of pages13
Original languageEnglish

Abstract

In this paper, we describe a framework for learning-based image enhancement. At the core of our algorithm lies a generic regularization framework that comprises a prior on natural images, as well as an application-specific conditional model based on Gaussian processes. In contrast to prior learning-based approaches, our algorithm can instantly learn task-specific degradation models from sample images which enables users to easily adopt the algorithm to a specific problem and data set of interest. This is facilitated by our
efficient approximation scheme of large-scale Gaussian processes. We demonstrate the efficiency and effectiveness of our approach by applying it to two example enhancement applications: single-image super-resolution as well as artifact removal in JPEG-encoded images.