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

Research output: Contribution to journalJournal article

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Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes. / Kwon, Younghee; Kim, Kwang In; Tompkin, James; Kim, Jin H.; Theobalt, Christian.

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 9, 01.09.2015, p. 1792-1805.

Research output: Contribution to journalJournal article

Harvard

Kwon, Y, Kim, KI, Tompkin, J, Kim, JH & Theobalt, C 2015, 'Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 9, pp. 1792-1805. https://doi.org/10.1109/TPAMI.2015.2389797

APA

Kwon, Y., Kim, K. I., Tompkin, J., Kim, J. H., & Theobalt, C. (2015). Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), 1792-1805. https://doi.org/10.1109/TPAMI.2015.2389797

Vancouver

Kwon Y, Kim KI, Tompkin J, Kim JH, Theobalt C. Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015 Sep 1;37(9):1792-1805. https://doi.org/10.1109/TPAMI.2015.2389797

Author

Kwon, Younghee ; Kim, Kwang In ; Tompkin, James ; Kim, Jin H. ; Theobalt, Christian. / Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015 ; Vol. 37, No. 9. pp. 1792-1805.

Bibtex

@article{c2db48cd9d58439ba6c042e1c1d0f665,
title = "Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes",
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 enoughto 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.",
keywords = "Gaussian process, Image enhancement, image compression, regression, super-resolution",
author = "Younghee Kwon and Kim, {Kwang In} and James Tompkin and Kim, {Jin H.} and Christian Theobalt",
year = "2015",
month = "9",
day = "1",
doi = "10.1109/TPAMI.2015.2389797",
language = "English",
volume = "37",
pages = "1792--1805",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
issn = "0162-8828",
publisher = "IEEE Computer Society",
number = "9",

}

RIS

TY - JOUR

T1 - Efficient learning of image super-resolution and compression artifact removal with semi-local Gaussian processes

AU - Kwon, Younghee

AU - Kim, Kwang In

AU - Tompkin, James

AU - Kim, Jin H.

AU - Theobalt, Christian

PY - 2015/9/1

Y1 - 2015/9/1

N2 - 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 enoughto 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.

AB - 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 enoughto 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.

KW - Gaussian process

KW - Image enhancement

KW - image compression

KW - regression

KW - super-resolution

U2 - 10.1109/TPAMI.2015.2389797

DO - 10.1109/TPAMI.2015.2389797

M3 - Journal article

VL - 37

SP - 1792

EP - 1805

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

SN - 0162-8828

IS - 9

ER -