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231224s2015 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2015.2473098
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|a eng
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|a Lu, Xin
|e verfasserin
|4 aut
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|a Image-Specific Prior Adaptation for Denoising
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|c 2015
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|a ƒaComputermedien
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|a Date Completed 03.02.2016
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|a Date Revised 27.01.2016
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Image priors are essential to many image restoration applications, including denoising, deblurring, and inpainting. Existing methods use either priors from the given image (internal) or priors from a separate collection of images (external). We find through statistical analysis that unifying the internal and external patch priors may yield a better patch prior. We propose a novel prior learning algorithm that combines the strength of both internal and external priors. In particular, we first learn a generic Gaussian mixture model from a collection of training images and then adapt the model to the given image by simultaneously adding additional components and refining the component parameters. We apply this image-specific prior to image denoising. The experimental results show that our approach yields better or competitive denoising results in terms of both the peak signal-to-noise ratio and structural similarity
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|a Lin, Zhe
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|a Jin, Hailin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Jianchao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, James Z
|e verfasserin
|4 aut
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|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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|g 24(2015), 12 vom: 26. Dez., Seite 5469-78
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|u http://dx.doi.org/10.1109/TIP.2015.2473098
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