The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
이 편지에서 우리는 자연 이미지의 풍부한 사전을 모델링하기 위한 과잉완전 필터를 배웠습니다. 우리의 접근 방식은 FOE(Fields of Experts)를 기반으로 한 빠른 근사 모델인 GSM FOE(Gaussian Scale Mixture Fields of Experts)를 확장합니다. 이러한 이전 이미지 이전 모델에서는 과도한 계산으로 인해 과잉 완료 사례가 고려되지 않습니다. 우리는 GSM FOE에 준직교성 가정을 도입하여 자연 이미지의 과도하게 완전한 필터를 빠르고 효율적으로 학습할 수 있습니다. 시뮬레이션에서는 이렇게 얻은 과잉 완성 필터가 Fields of Experts의 속성과 유사한 속성을 갖고 있음을 보여 주며, 잡음 제거 실험에서도 우리 모델의 우수성을 보여줍니다.
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부
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
부
@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},}
부
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 -