Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes

Kwon, Younghee and Kim, Kwang In and Tompkin, James and Kim, Jin H. and Theobalt, Christian (2015) Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (9). pp. 1792-1805.

Full text not available from this repository.

Abstract

Improving the quality of degraded images is a key problem in image processing, but the breadth of the problem leads to domain-specific approaches for tasks such as super-resolution and compression artifact removal. Recent approaches have shown that a general approach is possible by learning application-specific models from examples; however, learning models sophisticated enough to generate high-quality images is computationally expensive, and so specific per-application or per-dataset models are impractical. To solve this problem, we present an efficient semi-local approximation scheme to large-scale Gaussian processes. This allows efficient learning of task-specific image enhancements from example images without reducing quality. As such, our algorithm can be easily customized to specific applications and datasets, and we show the efficiency and effectiveness of our approach across five domains: single-image super-resolution for scene, human face, and text images, and artifact removal in JPEG- and JPEG 2000-encoded images.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1707
Subjects:
ID Code:
72535
Deposited By:
Deposited On:
22 Jan 2015 10:15
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Sep 2020 02:03