IEICE TRANSACTIONS on Information
A Fast Algorithm for Learning the Overcomplete Image Prior
Summary :
In this letter, we learned overcomplete filters to model rich priors of nature images. Our approach extends the Gaussian Scale Mixture Fields of Experts (GSM FOE), which is a fast approximate model based on Fields of Experts (FOE). In these previous image prior model, the overcomplete case is not considered because of the heavy computation. We introduce the assumption of quasi-orthogonality to the GSM FOE, which allows us to learn overcomplete filters of nature images fast and efficiently. Simulations show these obtained overcomplete filters have properties similar with those of Fields of Experts', and denoising experiments also show the superiority of our model.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.2 pp.403-406
- Publication Date
- 2010/02/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.403
- Type of Manuscript
- LETTER
- Category
- Image Processing and Video Processing
Authors
Keyword
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Zhe WANG, Siwei LUO, Liang WANG, "A Fast Algorithm for Learning the Overcomplete Image Prior" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 2, pp. 403-406, February 2010, doi: 10.1587/transinf.E93.D.403.
Abstract: In this letter, we learned overcomplete filters to model rich priors of nature images. Our approach extends the Gaussian Scale Mixture Fields of Experts (GSM FOE), which is a fast approximate model based on Fields of Experts (FOE). In these previous image prior model, the overcomplete case is not considered because of the heavy computation. We introduce the assumption of quasi-orthogonality to the GSM FOE, which allows us to learn overcomplete filters of nature images fast and efficiently. Simulations show these obtained overcomplete filters have properties similar with those of Fields of Experts', and denoising experiments also show the superiority of our model.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.403/_p
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@ARTICLE{e93-d_2_403,
author={Zhe WANG, Siwei LUO, Liang WANG, },
journal={IEICE TRANSACTIONS on Information},
title={A Fast Algorithm for Learning the Overcomplete Image Prior},
year={2010},
volume={E93-D},
number={2},
pages={403-406},
abstract={In this letter, we learned overcomplete filters to model rich priors of nature images. Our approach extends the Gaussian Scale Mixture Fields of Experts (GSM FOE), which is a fast approximate model based on Fields of Experts (FOE). In these previous image prior model, the overcomplete case is not considered because of the heavy computation. We introduce the assumption of quasi-orthogonality to the GSM FOE, which allows us to learn overcomplete filters of nature images fast and efficiently. Simulations show these obtained overcomplete filters have properties similar with those of Fields of Experts', and denoising experiments also show the superiority of our model.},
keywords={},
doi={10.1587/transinf.E93.D.403},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - A Fast Algorithm for Learning the Overcomplete Image Prior
T2 - IEICE TRANSACTIONS on Information
SP - 403
EP - 406
AU - Zhe WANG
AU - Siwei LUO
AU - Liang WANG
PY - 2010
DO - 10.1587/transinf.E93.D.403
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2010
AB - In this letter, we learned overcomplete filters to model rich priors of nature images. Our approach extends the Gaussian Scale Mixture Fields of Experts (GSM FOE), which is a fast approximate model based on Fields of Experts (FOE). In these previous image prior model, the overcomplete case is not considered because of the heavy computation. We introduce the assumption of quasi-orthogonality to the GSM FOE, which allows us to learn overcomplete filters of nature images fast and efficiently. Simulations show these obtained overcomplete filters have properties similar with those of Fields of Experts', and denoising experiments also show the superiority of our model.
ER -
English
