中国科学院机构知识库网格系统: 检索

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	PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural Networks 期刊论文 OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 页码: 13 作者: Xiao, Anqi; Shen, Biluo; Tian, Jie; Hu, Zhenhua \| 收藏 \| 浏览/下载：24/0 \| 提交时间：2023/11/17 Convolution Search problems Optimization Computer architecture Object detection Molecular imaging Visualization Multiscale neural architecture search (NAS) plug-and-play representation learning
	A Priori Land Surface Reflectance Synergized With Multiscale Features Convolution Neural Network for MODIS Imagery Cloud Detection 期刊论文 OAI收割 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2023, 卷号: 16, 页码: 3294-3308 作者: Ma, Nan; Sun, Lin; Zhou, Chenghu; He, Yawen; Dong, Chuanxiang \| 收藏 \| 浏览/下载：12/0 \| 提交时间：2023/10/09 Cloud detection difference-based samples land surface reflectance (LSR) dataset moderate resolution imaging spectrometer (MODIS) multiscale feature
	Design and realization of catadioptric long wave infrared multiscale optical system 期刊论文 OAI收割 Infrared Physics and Technology, 2022, 卷号: 123 作者: Yang, Shen; Hu, Wang; Yaoke, Xue; Yang, Song; Yongjie, Xie \| 收藏 \| 浏览/下载：40/0 \| 提交时间：2022/04/02 Multiscale optical system The catadioptric system Remote sensing Imaging experiment of prototype
	Optical design of monocentric multiscale three-line array airborne mapping camera 会议论文 OAI收割 Shanghai, China, 2021-10-28 作者: Yan, Aqi; Dong, Sen; Wu, Dengshan \| 收藏 \| 浏览/下载：26/0 \| 提交时间：2022/03/18 Optical design Monocentric multiscale imaging Three linear array aerial camera Stereo mapping Large field of view High resolution
	Multiscale characterization of three-dimensional pore structures in a shale gas reservoir: A case study of the Longmaxi shale in Sichuan basin, China 期刊论文 OAI收割 JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING, 2019, 卷号: 66, 页码: 207-216 作者: Wang, Yu; Wang, Lihua; Wang, Jianqiang; Jiang, Zheng; Wang, Chun-Chieh \| 收藏 \| 浏览/下载：84/0 \| 提交时间：2019/06/10 Multiscale imaging X-ray computed tomography FIB-SEM 3D microstructure Representative elementary volume Longmaxi shale
	Multiscale Fully Convolutional Network for Foreground Object Detection in Infrared Videos 期刊论文 OAI收割 Ieee Geoscience and Remote Sensing Letters, 2018, 卷号: 15, 期号: 4, 页码: 617-621 作者: Zeng, D. D.; Zhu, M. \| 收藏 \| 浏览/下载：14/0 \| 提交时间：2019/09/17 Background subtraction infrared (IR) object detection IR video surveillance multiscale fully convolutional network (MFCN) Geochemistry & Geophysics Engineering Remote Sensing Imaging Science & Photographic Technology
	CR image filter methods research based on wavelet-domain hidden markov models (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wang J.-L.; Wang J.-L.; Li D.-Y.; Wang Y.-P. 收藏 \| 浏览/下载：18/0 \| 提交时间：2013/03/25 In the procedure of computed radiography imaging we should firstly get across the characters of kinds of noises and the relationship between the image signals and noises. Based on the specialties of computed radiography (CR) images and medical image processing we have study the filtering methods for computed radiography images noises. On the base of analyzing computed radiography imaging system in detail the author think that the major two noises are Gaussian white noise and Poisson noise. Then the different relationship of between two kinds of noises and signal were studied completely. By considering both the characteristics of computed radiography images and the statistical features of wavelet transformed images a multiscale image filtering algorithm which based on two-state hidden markov model (HMM) and mixture Gaussian statistical model has been used to decrease the Gaussian white noise in computed images. By using EM (Expectation Maximization) algorithm to estimate noise coefficients in each scale and obtain power spectrum matrix then this carried through the syncretized two Filter that are IIR(infinite impulse response) Wiener Filter and HMM according to scale size and achieve the experiments as well as the comparison with other denoising methods were presented at last.