Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31531
Title: Single image super-resolution using Gaussian process regression
Authors: He, H
Siu, WC 
Issue Date: 2011
Source: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, 5995713, p. 449-456 How to cite?
Abstract: In this paper we address the problem of producing a high-resolution image from a single low-resolution image without any external training set. We propose a framework for both magnification and deblurring using only the original low-resolution image and its blurred version. In our method, each pixel is predicted by its neighbors through the Gaussian process regression. We show that when using a proper covariance function, the Gaussian process regression can perform soft clustering of pixels based on their local structures. We further demonstrate that our algorithm can extract adequate information contained in a single low-resolution image to generate a high-resolution image with sharp edges, which is comparable to or even superior in quality to the performance of other edge-directed and example-based super-resolution algorithms. Experimental results also show that our approach maintains high-quality performance at large magnifications.
Description: 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, Colorado Springs, CO, 20-25 June 2011
URI: http://hdl.handle.net/10397/31531
ISBN: 9781457703942
ISSN: 1063-6919
DOI: 10.1109/CVPR.2011.5995713
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