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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

  • Younghee Kwon
  • Kwang In Kim
  • James Tompkin
  • Jin H. Kim
  • Christian Theobalt
<mark>Journal publication date</mark>1/09/2015
<mark>Journal</mark>IEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number9
Number of pages14
Pages (from-to)1792-1805
Publication StatusPublished
Early online date9/01/15
<mark>Original language</mark>English


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.