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Efficient Learning-based Image Enhancement K. I. Kim, Y. Kwon, J. Tompkin, J.-H. Kim, and C. Theobalt About || Examples
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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 learningbased 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 adopt 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 singleimage super-resolution, as well as artifact removal in JPEG- and JPEG
2000-encoded images.
Details can be found in [KK12][KK14].
JPEG 2000 compression artifact removal
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JPEG 2000
(Comp. Ratio 0.15BPP) |
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[RB09] |
[NO03] |
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[GW05] |
[KK14]
(our method) |
JPEG compression artifact removal
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JPEG (Q2,
see [KK12]) |
[NO01] |
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[FK07] |
[KK14]
(our method) |
Single-image super-resolution (generic)
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N/A |
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Bicubic resampling; magnification factors 2 (left) and 3
(right) |
[FJ02] |
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N/A |
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[CY04] |
[YW10] |
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[KK10] |
[KK14]
(our method) |
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Single-image super-resolution (faces)
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Bicubic resampling; |
[KK10] |
[KK14]
(our method; |
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[KK10] |
K. I. Kim
and Y. Kwon, Single-image
super-resolution using sparse regression and natural image prior,
IEEE TPAMI, 2010. |
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[NO01] |
A. Nosratinia. Denoising
of JPEG images by re-application of JPEG, Journal
of VLSI Signal Processing, 2001. |
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[FJ02] |
W. T. Freeman, T. R. Jones, and E. C. Pasztor,
Example-based super-resolution, IEEE
CGA, 2002. |
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[CY04] |
H. Chang, D.-Y. Yeung, and Y. Xiong,
Super-resolution through neighbor embedding, CVPR, 2004. |
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[YW10] |
J. Yang, J. Wright, T. S. Huang, and Y. Ma, Image super-resolution
via sparse representation, IEEE TIP,
2010. |
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[NO03] |
A. Nosratinia, Postprocessing
of JPEG-2000 images to remove compression artifacts,
IEEE SPL, 2003. |
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[FK07] |
A. Foi, V. Katkovnik,
and K. Egiazarian, Pointwise
shape-adaptive DCT for high-quality denoising and deblocking of grayscale and color
images, IEEE TIP, 2007. |
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[RB09] |
S. Roth and M. J. Black, Fields of experts, IJCV, 2009. |
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[GW05] |
P. V. Gehler and M.Welling,
Product of �edge-perts�, NIPS, 2005. |
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[KK12] |
Y. Kwon, K. I. Kim, J. H. Kim, and C. Theobalt,
Efficient learning-based
image enhancement: application to super-resolution and compression artifact removal, BMVC, 2012. |
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[KK14] |
Y. Kwon, K. I. Kim, J. Tompkin, J.
H. Kim, and C. Theobalt, Efficient learning of
image super-resolution and compression artifact
removal with semi-local Gaussian processes, TPAMI, Accepted. |
Kwang In Kim: kkim at mpi-inf.mpg.de.
Younghee Kwon: youngheek at google.com.
James Tompkin: jtompkin at
seas.harvard.edu.