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

Younghee Kwon, Kwang In Kim, James Tompkin, Jin H. Kim, Christian Theobalt

Research output: Contribution to journalArticle

43 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1792-1805
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume37
Issue number9
Early online date8 Jan 2015
DOIs
Publication statusPublished - 1 Sep 2015

Keywords

  • Image enhancement
  • super-resolution
  • image compression
  • Gaussian process
  • regression

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