Home > Research > Publications & Outputs > Single-image super-resolution using sparse regr...
View graph of relations

Single-image super-resolution using sparse regression and natural image prior

Research output: Contribution to journalJournal article

Published

Standard

Single-image super-resolution using sparse regression and natural image prior. / Kim, Kwang In; Kwon, Younghee.

In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 6, 2010, p. 1127-1133.

Research output: Contribution to journalJournal article

Harvard

Kim, KI & Kwon, Y 2010, 'Single-image super-resolution using sparse regression and natural image prior', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 6, pp. 1127-1133. https://doi.org/10.1109/TPAMI.2010.25

APA

Kim, K. I., & Kwon, Y. (2010). Single-image super-resolution using sparse regression and natural image prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(6), 1127-1133. https://doi.org/10.1109/TPAMI.2010.25

Vancouver

Kim KI, Kwon Y. Single-image super-resolution using sparse regression and natural image prior. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010;32(6):1127-1133. https://doi.org/10.1109/TPAMI.2010.25

Author

Kim, Kwang In ; Kwon, Younghee. / Single-image super-resolution using sparse regression and natural image prior. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2010 ; Vol. 32, No. 6. pp. 1127-1133.

Bibtex

@article{25e369a0d38e4fdd8da46b24462f4040,
title = "Single-image super-resolution using sparse regression and natural image prior",
abstract = "This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based on example pairs of input and output images. Kernel ridge regression (KRR) is adopted for this purpose. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as has been done in existing example-based algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing algorithms shows the effectiveness of the proposed method.",
keywords = "gradient methods, image matching, image resolution, regression analysis, Kernel ridge regression, gradient descent, kernel matching pursuit, natural image prior, sparse regression, Displays, Energy resolution, Image enhancement, Image resolution, Kernel, Machine learning, Machine learning algorithms, Matching pursuit algorithms, Spatial resolution, Testing, Computer vision, display algorithms., image enhancement, machine learning",
author = "Kim, {Kwang In} and Younghee Kwon",
year = "2010",
doi = "10.1109/TPAMI.2010.25",
language = "English",
volume = "32",
pages = "1127--1133",
journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",
issn = "0162-8828",
publisher = "IEEE Computer Society",
number = "6",

}

RIS

TY - JOUR

T1 - Single-image super-resolution using sparse regression and natural image prior

AU - Kim, Kwang In

AU - Kwon, Younghee

PY - 2010

Y1 - 2010

N2 - This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based on example pairs of input and output images. Kernel ridge regression (KRR) is adopted for this purpose. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as has been done in existing example-based algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing algorithms shows the effectiveness of the proposed method.

AB - This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based on example pairs of input and output images. Kernel ridge regression (KRR) is adopted for this purpose. To reduce the time complexity of training and testing for KRR, a sparse solution is found by combining the ideas of kernel matching pursuit and gradient descent. As a regularized solution, KRR leads to a better generalization than simply storing the examples as has been done in existing example-based algorithms and results in much less noisy images. However, this may introduce blurring and ringing artifacts around major edges as sharp changes are penalized severely. A prior model of a generic image class which takes into account the discontinuity property of images is adopted to resolve this problem. Comparison with existing algorithms shows the effectiveness of the proposed method.

KW - gradient methods

KW - image matching

KW - image resolution

KW - regression analysis

KW - Kernel ridge regression

KW - gradient descent

KW - kernel matching pursuit

KW - natural image prior

KW - sparse regression

KW - Displays

KW - Energy resolution

KW - Image enhancement

KW - Image resolution

KW - Kernel

KW - Machine learning

KW - Machine learning algorithms

KW - Matching pursuit algorithms

KW - Spatial resolution

KW - Testing

KW - Computer vision

KW - display algorithms.

KW - image enhancement

KW - machine learning

U2 - 10.1109/TPAMI.2010.25

DO - 10.1109/TPAMI.2010.25

M3 - Journal article

VL - 32

SP - 1127

EP - 1133

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

SN - 0162-8828

IS - 6

ER -