Journal of Applied Mathematics
Volume 2012 (2012), Article ID 435924, 13 pages
Research Article

Energy-Driven Image Interpolation Using Gaussian Process Regression

1Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
2School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China

Received 1 March 2012; Accepted 27 April 2012

Academic Editor: Baocang Ding

Copyright © 2012 Lingling Zi and Junping Du. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Image interpolation, as a method of obtaining a high-resolution image from the corresponding low-resolution image, is a classical problem in image processing. In this paper, we propose a novel energy-driven interpolation algorithm employing Gaussian process regression. In our algorithm, each interpolated pixel is predicted by a combination of two information sources: first is a statistical model adopted to mine underlying information, and second is an energy computation technique used to acquire information on pixel properties. We further demonstrate that our algorithm can not only achieve image interpolation, but also reduce noise in the original image. Our experiments show that the proposed algorithm can achieve encouraging performance in terms of image visualization and quantitative measures.