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  Efficient Learning-based Image Enhancement : Application to Compression Artifact Removal and Super-resolution

Kim, K. I., Kwon, Y., Kim, J. H., & Theobalt, C.(2011). Efficient Learning-based Image Enhancement: Application to Compression Artifact Removal and Super-resolution (MPI-I-2011-4-002). Saarbrücken: Max-Planck-Institut für Informatik.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0027-13A3-E Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0029-B3C7-F
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 Creators:
Kim, Kwang In1, Author           
Kwon, Younghee2, Author
Kim, Jin Hyung2, Author
Theobalt, Christian1, Author           
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1Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              
2External Organizations, ou_persistent22              

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 Abstract: Many computer vision and computational photography applications essentially solve an image enhancement problem. The image has been deteriorated by a specific noise process, such as aberrations from camera optics and compression artifacts, that we would like to remove. We describe a framework for learning-based image enhancement. At the core of our algorithm lies a generic regularization framework that comprises a prior on natural images, as well as an application-specific conditional model based on Gaussian processes. In contrast to prior learning-based approaches, our algorithm can instantly learn task-specific degradation models from sample images which enables users to easily adapt the algorithm to a specific problem and data set of interest. This is facilitated by our efficient approximation scheme of large-scale Gaussian processes. We demonstrate the efficiency and effectiveness of our approach by applying it to example enhancement applications including single-image super-resolution, as well as artifact removal in JPEG- and JPEG 2000-encoded images.

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Language(s): eng - English
 Dates: 2011
 Publication Status: Published online
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 Publishing info: Saarbrücken : Max-Planck-Institut für Informatik
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 Identifiers: Report Nr.: MPI-I-2011-4-002
BibTex Citekey: KimKwonKimTheobalt2011
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Title: Research Report
Source Genre: Series
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: ISSN: 0946-011X