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Image-type displacement measurement resolution improvement without magnification imaging 期刊论文
Measurement Science and Technology, 2022, 卷号: 33, 期号: 1, 页码: 7
作者:  H. Yu
收藏  |  浏览/下载：5/0  |  提交时间：2022/06/13
A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal 期刊论文
Applied Sciences-Basel, 2021, 卷号: 11, 期号: 2, 页码: 11
作者:  G. L. Ben;  X. F. Zheng;  Y. C. Wang;  N. Zhang and X. Zhang
收藏  |  浏览/下载：2/0  |  提交时间：2022/06/13
A sensitive methane sensor of a ppt detection level using a mid-infrared interband cascade laser and a long-path multipass cell 期刊论文
Sensors and Actuators, B: Chemical, 2021, 卷号: 334
作者:  J. Xia;  C. Feng;  F. Zhu;  S. Ye;  S. Zhang
收藏  |  浏览/下载：3/0  |  提交时间：2022/06/13
Feasibility of Laser Communication Beacon Light Compressed Sensing 期刊论文
Sensors, 2020, 卷号: 20, 期号: 24, 页码: 13
作者:  Z. Wang,S. J. Gao and L. Sheng
收藏  |  浏览/下载：3/0  |  提交时间：2021/07/06
Experimental Investigation of Iterative Pseudoinverse Ghost Imaging 期刊论文
Ieee Photonics Journal, 2018, 卷号: 10, 期号: 3
作者:  Lv, X. F.;  Guo, S. X.;  Wang, C. L.;  Yang, C.;  Zhang, H. W.
收藏  |  浏览/下载：7/0  |  提交时间：2019/09/17
基于FPGA的无创伤血液成分光谱数据采集系统设计 学位论文
硕士: 中国科学院大学, 2015
作者:  郭嘉
收藏  |  浏览/下载：32/0  |  提交时间：2016/04/11
Qualitative Identification of Fish Meal and Meat Bone Meal via Fluorescence Spectral Imaging 期刊论文
Food Analytical Methods, 2015, 卷号: 8, 期号: 8, 页码: 2150-2156
作者:  Li, Y. P.;  F. R. Huang;  R. Y. Xian;  X. S. Lu;  J. Dong
收藏  |  浏览/下载：17/0  |  提交时间：2016/07/15
拉曼光谱数据处理与定性分析技术研究 学位论文
博士: 中国科学院大学, 2014
姜承志
收藏  |  浏览/下载：351/0  |  提交时间：2014/08/21
Dictionary learning approach for image deconvolution with variance estimation 期刊论文
Applied Optics, 2014, 卷号: 53, 期号: 25, 页码: 5677-5684
Yang H.; Zhu M.; Wu X. T.; Zhang Z. B.; Huang H. Y.
收藏  |  浏览/下载：12/0  |  提交时间：2015/04/24
A new approach for the removal of mixed noise based on wavelet transform (EI CONFERENCE) 会议论文
ICO20: Remote Sensing and Infrared Devices and Systems, August 21, 2005 - August 26, 2005, Changchun, China
Li Y.; Ni H.; Pang W.; Hao Z.
收藏  |  浏览/下载：27/0  |  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.  

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