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	便携式心电监护终端动态心电检测的研究学位论文 OAI收割工学硕士, 中国科学院自动化研究所: 中国科学院研究生院, 2012 施帆收藏 \| 浏览/下载：44/0 \| 提交时间：2015/09/02 动态心电便携式心电监护心电检测小波变换自适应阈值判别 Dynamic ECG Portable ECG monitor Detection of DCG Wavelet Transform Adaptive-Thresholding
	The Application of Wavelet-Based Contourlet Transform on Compressed Sensing 会议论文 OAI收割 2012 International Conference on Multimedia and Signal Processing, Shanghai, China, December 7-9, 2012 作者: Du M(杜梅); Zhao HC(赵怀慈); Zhao CY(赵春阳); Li B(李波) 收藏 \| 浏览/下载：91/0 \| 提交时间：2012/12/28 Sparse Representation Wavelet-Based Contourlet Transform Block Compressed Sensing Iterative Hard Thresholding Algorithm
	Electro-optical imaging system identification using pseudo-random binary pattern (EI CONFERENCE) 会议论文 OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China Le Y.; Jian W.; Jianzhong Z.; Qiang S.; Jianzhuo L. 收藏 \| 浏览/下载：23/0 \| 提交时间：2013/03/25 A method using pseudo-random binary pattern is developed for electro-optical imaging system identification. The imaging system is taken as a stable linear time-invariant and causal filter and its transfer function is measured through the pseudo-random binary sequence impulse responses identification. A digital mirror device (DMD) light projector is developed as the target generator and wavelet thresholding is used to denoise the captured image. Pre-filtering spectral estimation algorithm with adaptive parameter selection is also proposed for the identification process overcoming the challenge brought by the size limit derived from the optical isoplantic region. Simulations and experiments are presented to show the effectiveness of the proposed method.0-64. 2010 IEEE.
	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.
	Color image denoising with wavelet thresholding based on human visual system model 期刊论文 OAI收割 SIGNAL PROCESSING-IMAGE COMMUNICATION, 2005, 卷号: 20, 期号: 2, 页码: 115-127 作者: Huang, KQ; Wu, ZY; Fung, GSK; Chan, FHY 收藏 \| 浏览/下载：28/0 \| 提交时间：2015/11/06 color image denoising wavelet thresholding human visual system model contrast sensitivity function masking