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	DC modelling in 2,5-D anisotropic media with singularity removal 期刊论文 OAI收割 Exploration Geophysics, 2019, 页码: 1-9 作者: Tao Song; Yun Liu; Yun Wang \| 收藏 \| 浏览/下载：10/0 \| 提交时间：2020/09/07 Singularity Removal Anisotropic Media Current Density Longitudinal Resistivity Transverse Resistivity
	DC modelling in 2.5-D anisotropic media with singularity removal 期刊论文 OAI收割 Exploration Geophysics, 2019, 卷号: 50, 期号: 3, 页码: 221-232 作者: Tao Song; Yun Liu; Yun Wang \| 收藏 \| 浏览/下载：17/0 \| 提交时间：2020/12/04 Singularity Removal anisotropic Media current Density longitudinal Resistivity transverse Resistivity
	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：36/0 \| 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.