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	Glutathione assisting the waste tobacco leaf to synthesize versatile biomass-based carbon dots for simultaneous detection and efficient removal of mercury ions 期刊论文 OAI收割 Journal of Environmental Chemical Engineering, 2022, 卷号: 10, 期号: 6, 页码: 108718 作者: Hui Yang; Xiankun Su; Li Cai; Zhenchun Sun; Yechun Lin \| 收藏 \| 浏览/下载：15/0 \| 提交时间：2023/03/29 Waste Tobacco Leaf Red Fluorescence Biomass-based Cds Hg2++ Detection And Removal Large Stokes Shift Emission
	A Noise Removal Algorithm Based on OPTICS for Photon-Counting LiDAR Data 期刊论文 OAI收割 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2021, 卷号: 18, 期号: 8, 页码: 1471-1475 作者: Zhu, Xiaoxiao; Nie, Sheng; Wang, Cheng; Xi, Xiaohuan; Wang, Jinsong \| 收藏 \| 浏览/下载：92/0 \| 提交时间：2021/08/19 Ice, Cloud, Land Elevation Satellite-2 (ICESat-2) noise removal ordering points to identify the clustering structure (OPTICS) photon-counting Light Detection and Ranging (LiDAR) (PCL)
	养猪场抗生素污染浓度水平调查及生物转化研究学位论文 OAI收割博士, 北京: 中国科学院研究生院, 2011 潘寻收藏 \| 浏览/下载：189/0 \| 提交时间：2011/08/30 抗生素畜禽养殖业粪污同步检测方法资源化处置生物处理工艺去除效率 Antibiotics Stockbreeding industry Manure and wastewater Simultaneous detection methods Resourceful disposition Biological treatment processes Removal efficiency
	Toward Accurate and Fast Iris Segmentation for Iris Biometrics 期刊论文 OAI收割 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2009, 卷号: 31, 期号: 9, 页码: 1670-1684 作者: He, Zhaofeng; Tan, Tieniu; Sun, Zhenan; Qiu, Xianchao 收藏 \| 浏览/下载：33/0 \| 提交时间：2015/08/12 Biometrics iris segmentation reflection removal eyelid localization eyelash and shadow detection edge fitting
	基于视频的交通数据采集关键技术及应用研究学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2008 作者: 王坤峰收藏 \| 浏览/下载：67/0 \| 提交时间：2015/09/02 智能交通系统摄像机标定运动目标检测与阴影去除虚拟线圈嵌入式交通数据采集系统 ITS Camera calibration Moving object detection and shadow removal Virtual loop Embedded traffic data collection system
	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：38/0 \| 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.