机构

• 地理科学与资源研究所 [3]
• 地质与地球物理研究所 [1]
• 长春光学精密机械与物... [1]
• 沈阳自动化研究所 [1]
• 上海光学精密机械研究... [1]

采集方式

• OAI收割 [7]

内容类型

• 期刊论文 [3]
• SCI/SSCI论文 [2]
• 会议论文 [2]

发表日期

• 2017 [2]
• 2016 [1]
• 2013 [2]
• 2008 [1]
• 2006 [1]

学科主题

筛选

浏览/检索结果: 共7条，第1-7条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Near real time de-noising of satellite-based soil moisture retrievals: An intercomparison among three different techniques 期刊论文  OAI收割 REMOTE SENSING OF ENVIRONMENT, 2017, 卷号: 198, 页码: 17-29 作者:  Massari, Christian;  Su, Chun-Hsu;  Brocca, Luca;  Sang, Yan-Fang;  Ciabatta, Luca   |  收藏  |  浏览/下载：27/0  |  提交时间：2019/09/25 De-noising  Wavelet  Satellite soil moisture observations  Near real time   Multi-threshold de-noising of electrical imaging logging data based on the wavelet packet transform 期刊论文  OAI收割 JOURNAL OF GEOPHYSICS AND ENGINEERING, 2017, 卷号: 14, 期号: 4, 页码: 900-908 作者:  Xie, Fang;  Xiao, Chengwen;  Liu, Ruilin;  Zhang, Lili   |  收藏  |  浏览/下载：27/0  |  提交时间：2017/11/24 wavelet packet transform  multi-threshold de-noising  electric imaging logging data  fractured-vuggy carbonatite reservoir   Online Non-destructive Detection of Jujuble to Classify Infested and Intact Groups Based on Near Infrared Diffuse Reflection Spectra Analysis Technique 会议论文  OAI收割 2016 2nd International Conference on Mechanical, Electronic and Information Technology Engineering, Chongqing, China, May 21-22, 2016 作者:  Zhang CX(张翠侠);  Ma Y(马钺) 收藏  |  浏览/下载：25/0  |  提交时间：2016/09/13 Non-destructive detection  Near infrared spectrometry  Date pretreatment  De-noising  Wavelet transform  Derivative  Mahalanobis distance discriminant   Improved continuous wavelet analysis of variation in the dominant period of hydrological time series SCI/SSCI论文  OAI收割 2013 作者:  Wang D. 收藏  |  浏览/下载：15/0  |  提交时间：2014/12/24 hydrological series analysis  continuous wavelet transform  wavelet  basis function  multi-temporal scale  wavelet spectrum  period  trend  change points  maximum entropy spectral analysis  de-noising  entropy spectral-analysis  decomposition  identification  transform  threshold  model   A review on the applications of wavelet transform in hydrology time series analysis SCI/SSCI论文  OAI收割 2013 Sang Y. F. 收藏  |  浏览/下载：32/0  |  提交时间：2014/12/24 Hydrologic time series analysis  Wavelet transform  Periodicity  Trend  De-noising  Complexity  Hydrologic forecasting  multiscale entropy analysis  artificial neural-networks  information-theory  noise-reduction  rainfall fields  trend analysis  river flow  model  uncertainty  multiresolution   时变带限信道中光通信的均衡与去噪技术 期刊论文  OAI收割 光子学报, 2008, 卷号: 37, 期号: 6, 页码: 1195, 1199 梁波; 朱海; 陈卫标 收藏  |  浏览/下载：909/147  |  提交时间：2009/09/18 Convergence (mathematics)  De noising  Digital equalization  Equalization techniques  Numerical simulations  Optical (PET) (OPET)  Optical pulses  Optical signalling  Poisson noise  Semi-blind equalization  Soft-threshold  Wavelet de noising   A new approach for the removal of mixed noise 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:  Li Y.;  Li Y.;  Li Y.;  Li Y. 收藏  |  浏览/下载：38/0  |  提交时间：2013/03/25 This paper proposed a new approach for the removal of mixed noise. There are many different ways in image denoising. Donoho et al have proposed a method for image de-noising by thresholding  ambiguity is often resulted in determining the correspondence of a modulus maximum to a singularity. In the light  and indeed  we combine the merits of the two techniques to form a new approach for the removal of mixed noise. At first  the application of their method to image denoising has been extremely successful. But the method of Donoho is based on the assumption that the type of noise is only additive Gaussian noise  we used wavelet singularity detection (WSD) technique to analyze singularities of signal and noise. According to the characteristic that wavelet transform modulus maxima of impulse noise rapidly decreases as the scale increases in wavelet domain  which is not successful for impulse noise. Mallat has also presented a method for signal denoising by discriminating the noise and the signal singularities through an analysis of their wavelet transform modulus maxima (WTMM). Nevertheless  it can be accurately located with multiscale space by going through dyadic orthogonal wavelet transform and removed. Furthermore the Gaussian noise is also removed through a level-dependent thresholding algorithm  the tracing of WTMM is not just tedious procedure computationally  algorithm. The experimental results demonstrate that the proposed method can effectively detect impulse noise and remove almost all of the noise while preserve image details very well.