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	Identification of Metal Components Characteristic Peak Position of Energy Dispersive X-Ray Fluorescence Spectra Based on the Wavelet Transformation 期刊论文 OAI收割 SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 卷号: 38, 期号: 6, 页码: 1904-1909 作者: Zhang Wei; Xu Hua; Duan Lian-fei; Ma Ming-jun; Gan Ting-ting \| 收藏 \| 浏览/下载：24/0 \| 提交时间：2019/11/12 X-ray fluorescence Wavelet transformation Modulus maxima Characteristic peak
	Directional multiscale edge detection using the contourlet transform (EI CONFERENCE) 会议论文 OAI收割 2010 IEEE International Conference on Advanced Computer Control, ICACC 2010, March 27, 2010 - March 29, 2010, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States 作者: Jin L.-X.; Han S.-L.; Zhang R.-F. 收藏 \| 浏览/下载：42/0 \| 提交时间：2013/03/25 Wavelet multiresolution analysis allows us to detect edges at different scales also to obtain other important aspects of the extracted edges. However due to the usual two-dimensional tensor product wavelet transform is not optimal for representing images. The main problem in edge detection using wavelet transform is that it can only capture point-singularities and the extracted edges are not continuous. In order to solve that problem we propose a new image edge detection method based on the contourlet transform. The directional multiresolution representation Contourlet takes advantages of the intrinsic geometrical structure of images and is appropriate for the analysis of the image edges. Using the modulus maxima detection an image edge detection method based on contourlet transform is proposed. To suppress the image noise effect on edge detection the scale multiplication in contourlet domain is also proposed. Through real images experiments the proposed edge detection method's performance for the extracted edges is analyzed and compared with other two edge detection methods. The experiment result proves that the proposed edge detection method improves over wavelet-based techniques and Canny detector and also works well for noisy images. 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：44/0 \| 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.