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Single image super-resolution using Gaussian process regression
| Content Provider | CiteSeerX |
|---|---|
| Author | He, He Siu, Wan-Chi |
| Description | In IEEE Conf. on Computer Vision and Pattern Recognition 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 orig-inal 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 regres-sion can perform soft clustering of pixels based on their local structures. We further demonstrate that our algo-rithm can extract adequate information contained in a sin-gle low-resolution image to generate a high-resolution im-age with sharp edges, which is comparable to or even supe-rior in quality to the performance of other edge-directed and example-based super-resolution algorithms. Experi-mental results also show that our approach maintains high-quality performance at large magnifications. 1. |
| File Format | |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
