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浏览/检索结果: 共7条,第1-7条 帮助

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Kernel reconstruction learning 期刊论文  OAI收割
NEUROCOMPUTING, 2023, 卷号: 522, 页码: 1-10
作者:  
Wu, Yun;  Xiong, Shifeng
  |  收藏  |  浏览/下载:35/0  |  提交时间:2023/02/07
On construction of prediction intervals for heteroscedastic regression 期刊论文  OAI收割
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2022, 页码: 26
作者:  
Wu, Yun;  Xiong, Shifeng
  |  收藏  |  浏览/下载:16/0  |  提交时间:2023/02/07
Spatiotemporal Distribution and Geographical Impact Factors of Barley and Wheat during the Late Neolithic and Bronze Age (4000-2300 cal. a BP) in the Gansu-Qinghai Region, Northwest China 期刊论文  OAI收割
SUSTAINABILITY, 2022, 卷号: 14, 期号: 9, 页码: 17
作者:  
Ma, Zhikun;  Song, Jincheng;  Wu, Xiaohui;  Hou, Guangliang;  Huan, Xiujia
  |  收藏  |  浏览/下载:67/0  |  提交时间:2022/07/18
Optimal design for kernel interpolation: Applications to uncertainty quantification 期刊论文  OAI收割
JOURNAL OF COMPUTATIONAL PHYSICS, 2021, 卷号: 430, 页码: 19
作者:  
Narayan, Akil;  Yan, Liang;  Zhou, Tao
  |  收藏  |  浏览/下载:19/0  |  提交时间:2021/04/26
A New Indicator to Assess Public Perception of Air Pollution Based on Complaint Data 期刊论文  OAI收割
APPLIED SCIENCES-BASEL, 2021, 卷号: 11, 期号: 4, 页码: 17
作者:  
Sun, Yong;  Jin, Fengxiang;  Zheng, Yan;  Ji, Min;  Wang, Huimeng
  |  收藏  |  浏览/下载:40/0  |  提交时间:2021/06/10
A high-speed multi-scale kernel correlation filter tracking algorithm 会议论文  OAI收割
Guangzhou, China, 2019-05-10
作者:  
Fu, Bin;  Song, Zongxi;  Wang, Feng;  Gao, Wei;  Zhang, Shuang
  |  收藏  |  浏览/下载:62/0  |  提交时间:2019/10/10
A MLP-PNN neural network for CCD image super-resolution in wavelet packet domain (EI CONFERENCE) 会议论文  OAI收割
2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, October 12, 2008 - October 14, 2008, Dalian, China
Zhao X.; Fu D.; Zhai L.
收藏  |  浏览/下载:70/0  |  提交时间:2013/03/25
Image super-resolution methods process an input image sequence of a scene to obtain a still image with increased resolution. Classical approaches to this problem involve complex iterative minimization procedures  typically with high computational costs. In this paper is proposed a novel algorithm for super-resolution that enables a substantial decrease in computer load. First  decompose and reconstruct the image by wavelet packet. Before constructing the image  use neural network in place of other rebuilding method to reconstruct the coefficients in the wavelet packet domain. Second  probabilistic neural network architecture is used to perform a scattered-point interpolation of the image sequence data in the wavelet packet domain. The network kernel function is optimally determined for this problem by a MLP-PNN (Multi Layer Perceptron - Probabilistic Neural Network) trained on synthetic data. Network parameters dependent on the sequence noise level. This super-sampled image is spatially Altered to correct finite pixel size effects  to yield the final high-resolution estimate. This method can decrease the calculation cost and get perfect PSNR. Results are presented  showing the quality of the proposed method. 2008 IEEE.