中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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微震信号FIR–小波联合滤波算法及应用 期刊论文  OAI收割
岩石力学与工程学报, 2020, 卷号: 39, 期号: 9, 页码: 1872
作者:  
朱新豪;  陈炳瑞;  李涛;  魏凡博;  王旭
  |  收藏  |  浏览/下载:14/0  |  提交时间:2021/05/25
A SURE Based Sub-band Adaptive De-noising Method 会议论文  OAI收割
2016 2nd IEEE International Conference on Computer and Communications (ICCC 2016), Chengdu, China, October 14-17, 2016
作者:  
Gong TR(宫铁瑞);  Yang ZJ(杨志家);  Wang GS(王庚善);  Jiao P(焦平)
收藏  |  浏览/下载:35/0  |  提交时间:2016/12/21
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:68/0  |  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.  
CR image filter methods research based on wavelet-domain hidden markov models (EI CONFERENCE) 会议论文  OAI收割
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
作者:  
Wang J.-L.;  Wang J.-L.;  Li D.-Y.;  Wang Y.-P.
收藏  |  浏览/下载:18/0  |  提交时间:2013/03/25
In the procedure of computed radiography imaging  we should firstly get across the characters of kinds of noises and the relationship between the image signals and noises. Based on the specialties of computed radiography (CR) images and medical image processing  we have study the filtering methods for computed radiography images noises. On the base of analyzing computed radiography imaging system in detail  the author think that the major two noises are Gaussian white noise and Poisson noise. Then  the different relationship of between two kinds of noises and signal were studied completely. By considering both the characteristics of computed radiography images and the statistical features of wavelet transformed images  a multiscale image filtering algorithm  which based on two-state hidden markov model (HMM) and mixture Gaussian statistical model  has been used to decrease the Gaussian white noise in computed images. By using EM (Expectation Maximization) algorithm to estimate noise coefficients in each scale and obtain power spectrum matrix  then this carried through the syncretized two Filter that are IIR(infinite impulse response) Wiener Filter and HMM  according to scale size  and achieve the experiments as well as the comparison with other denoising methods were presented at last.  
On orbit MTF measurement of remote sensing camera based on wavelet transform (EI CONFERENCE) 会议论文  OAI收割
ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China
作者:  
Wang X.;  Wang X.;  Wang X.
收藏  |  浏览/下载:18/0  |  提交时间:2013/03/25
Asymptotically efficient estimation based on wavelet of expectation value in a partial linear model 期刊论文  OAI收割
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 1999, 卷号: 28, 期号: 9, 页码: 2045-2055
作者:  
Liang, H;  Zhu, LX;  Zhou, Y
  |  收藏  |  浏览/下载:18/0  |  提交时间:2018/07/30