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Lidar signal de-noising based on wavelet trimmed thresholding technique

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Chinese Optics Letters 2004年第1期2卷 1-3页

作者：方海涛黄德双 Institute of Intelligent Machines Chinese Academy of Sciences Hefei 230031 Automatization Department of University of Science and Technology of China Hefei 230026 Institute of Intelligent Machines Chinese Academy of Sciences Hefei 230031

Lidar is an efficient tool for remote monitoring, but the effective range is often limited by signal-to-noise ratio (SNR). By the power spectral estimation, we find that digital filters are not fit for processing lidar signals buried in noise. In this paper, we present a new method of the lidar signal acquisition based on the wavelet trimmed thresholding technique to increase the effective range of lidar measurements. The performance of our method is investigated by detecting the real signals in noise. The experiment results show that our approach is superior to the traditional methods such as Butterworth filter.

关键词： noise abatement Signal to noise ratio Wavelet transforms

Adaptively wavelet-based image denoising algorithm with edge preserving

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Chinese Optics Letters 2006年第2期4卷 80-83页

作者：谭毅华田金文柳健 State Key Laboratory of Education Ministry for Image Processing and Intelligent Control

A new wavelet-based image denoising algorithm, which exploits the edge information hidden in the corrupted image, is presented. Firstly, a canny-like edge detector identifies the edges in each subband. Secondly, multiplying the wavelet coefficients in neighboring scales is implemented to suppress the noise while magnifying the edge information, and the result is utilized to exclude the fake edges. The isolated edge pixel is also identified as noise. Unlike the thresholding method, after that we use local window filter in the wavelet domain to remove noise in which the variance estimation is elaborated to utilize the edge intbrmation. This method is adaptive to local image details, and can achieve bet, ter performance than the methods of state of the art.

关键词： Adaptive algorithms Detectors Edge detection Information technology noise abatement Pattern recognition Wavelet transforms

Denoising lidar signal by combining wavelet improved threshold with wavelet domain spatial filtering

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Chinese Optics Letters 2006年第12期4卷 694-696页

作者：尹世荣王蔚然 School of Electronic Engineering University of Electronic Science and Technology of ChinaChengdu 610054

Lidar is an effective tool for remotely monitoring target or object, but the lidar signal is often affected by various noises or interferences. Therefore, detecting the weak signals buried in noises is a fundamental and important problem in the lidar systems. In this paper, an effective noise reduction method combining wavelet improved threshold with wavelet domain spatial filtration is presented to denoise pulse lidar signal and is investigated by detecting the simulating pulse lidar signals in noise. The simulation results show that this method can effectively identify the edge of signal and detect the weak lidar signal buried in noises.

关键词： Monitoring noise abatement Optical radar Signal interference Simulation

De-noising of Raman spectrum signal based on stationary wavelet transform

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Chinese Optics Letters 2004年第2期2卷 113-115页

作者：高清维孙兆奇曹卓良程蒲 Department of Automation Anhui University Hefei 230039 Department of Physics Anhui University Hefei 230039

In this paper, the Raman spectrum signal de-noising based on stationary wavelet transform is discussed. Haar wavelet is selected to decompose the Raman spectrum signal for several levels based on stationary wavelet transform. The noise mean square j is estimated by the wavelet details at every level, and the wavelet details toward 0 by a threshold , where n is length of the detail, then recovery signal is reconstructed. Experimental results show this method not only suppresses noise effectively, but also preserves as many target characteristics of original signal as possible. This de-noising method offers a very attractive alternative to Raman spectrum signal noise suppress.

关键词： noise abatement Signal processing Wavelet transforms