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

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44776

Kim,  Kwang In
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45610

Theobalt,  Christian
Computer Graphics, MPI for Informatics, Max Planck Society;

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Fulltext (public)

MPI-I-2011-4-002.pdf
(Any fulltext), 7MB

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0027-13A3-E
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.