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	Automatic P-Phase-Onset-Time-Picking Method of Microseismic Monitoring Signal of Underground Mine Based on Noise Reduction and Multiple Detection Indexes 期刊论文 OAI收割 ENTROPY, 2023, 卷号: 25, 期号: 10, 页码: 16 作者: Dai, Rui; Wang, Yibo; Zhang, Da; Ji, Hu \| 收藏 \| 浏览/下载：30/0 \| 提交时间：2023/12/27 microseisms P-phase onset time picking STA/LTA method AIC method skew and kurtosis method wavelet coefficient threshold denoising in time-frequency domain
	Noise removal for semi-airborne data using wavelet threshold and singular value decomposition 期刊论文 OAI收割 JOURNAL OF APPLIED GEOPHYSICS, 2022, 卷号: 201, 页码: 11 作者: Lv, Pengfei; Wu, Xin; Zhao, Yang; Chang, Jianghao \| 收藏 \| 浏览/下载：64/0 \| 提交时间：2022/07/18 Semi-airborne transient electromagnetic method Short-time sliding window Singular spectrum decomposition Wavelet threshold method Noise processing
	Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China Wu Z.-G.; Wang M.-J.; Han G.-L. 收藏 \| 浏览/下载：81/0 \| 提交时间：2013/03/25 Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.
	An improved destripe noises method for TDI-CCD images (EI CONFERENCE) 会议论文 OAI收割 2011 International Conference on Mechatronic Science, Electric Engineering and Computer, MEC 2011, August 19, 2011 - August 22, 2011, Jilin, China 作者: He B. 收藏 \| 浏览/下载：25/0 \| 提交时间：2013/03/25 The new destriping method using lifting wavelet transform by means of the improved threshold function is presented in this letter. It can overcome the deficiency of the hard and soft threshold function. As compared with the known threshold functions the quality of the denoised images using the improved threshold function is much better. The results based on several image quality indexes present that the destriped images are not only visually more plausible but also suitable for analysis. Also it is reasonable in computer time and storage space to use the lifting wavelet transform. 2011 IEEE.
	Destriping method using lifting wavelet transform of remote sensing image (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 作者: He B. 收藏 \| 浏览/下载：33/0 \| 提交时间：2013/03/25 Based on the characteristic of striping noise in remote sensing images a new destriping noise technique for the improved multi-threshold method using lifting wavelet transform applied to remote sensing imagery is presented in this letter. Have used the lifting wavelet decomposition algorithm the thresholds are determined by corresponding wavelet coefficients in every scale. Remote sensing imagery is so large that the algorithm must be fast and effective. The lifting wavelet transform is easily realized and inexpensive in computer time and storage space compared with the traditional wavelet transform. We also compare the method with some traditional destriping methods both by visual inspection and by appropriate indexes of quality of the denoised images. From the comparison we can see that the adaptive threshold method can preserve the spectral characteristic of the images while effectively remove striping noise and it did better than the existed ones. 2010 IEEE.
	Study of removing striping noise in CCD image (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Liu H.; Liu H.; Liu H. 收藏 \| 浏览/下载：18/0 \| 提交时间：2013/03/25 Striping noise is the common system noise during formation of image using linear array CCD and has the character of periodicity directivity and banding distributing. It can be caused by errors in internal calibration devices or by slight gain/offset differences among the elements that conform the array of detectors. Striping noise covers up useful information in CCD image and brings adverse effect to image interpretation. On the basis of analyzing wavelet decomposed coefficient the regularities of distribution about striping noise in wavelet coefficient is found thereby the method of wavelet threshold selection which is suitable to striping noise distribution is put forward. According to Donoho's method about denoising using wavelet the image including striping noise is processed. Comparing the power spectrum of processed image with the one of original image polluted by striping noise in frequency field we find pulse brought by striping noise is removed and the goal which reserves image details and reduces stripes is achieved.