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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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Kim, Kwang In1, Autor           
Kwon, Younghee2, Autor
Kim, Jin Hyung2, Autor
Theobalt, Christian1, Autor                 
Affiliations:
1Computer Graphics, MPI for Informatics, Max Planck Society, ou_40047              
2External Organizations, ou_persistent22              

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 Zusammenfassung: 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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Sprache(n): eng - English
 Datum: 2011
 Publikationsstatus: Online veröffentlicht
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 Ort, Verlag, Ausgabe: Saarbrücken : Max-Planck-Institut für Informatik
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 Identifikatoren: Reportnr.: MPI-I-2011-4-002
BibTex Citekey: KimKwonKimTheobalt2011
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Titel: Research Report
Genre der Quelle: Reihe
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