中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
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浏览/检索结果: 共14条,第1-10条 帮助

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SAC-GAN: Structure-Aware Image Composition 期刊论文  OAI收割
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2024, 卷号: 30, 期号: 7, 页码: 3151-3165
作者:  
Zhou, Hang;  Ma, Rui;  Zhang, Ling-Xiao;  Gao, Lin;  Mahdavi-Amiri, Ali
  |  收藏  |  浏览/下载:22/0  |  提交时间:2024/12/06
Reference-Based Deep Line Art Video Colorization 期刊论文  OAI收割
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, 2023, 卷号: 29, 期号: 6, 页码: 2965-2979
作者:  
Shi, Min;  Zhang, Jia-Qi;  Chen, Shu-Yu;  Gao, Lin;  Lai, Yu-Kun
  |  收藏  |  浏览/下载:37/0  |  提交时间:2023/12/04
Enhancing MTInSAR Phase Unwrapping in Decorrelating Environments by Spatiotemporal Observation Optimization 期刊论文  OAI收割
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2023, 卷号: 20, 页码: 4002505
作者:  
Liang, Hongyu;  Zhang, Lei;  Li, Xin
  |  收藏  |  浏览/下载:28/0  |  提交时间:2024/01/04
Millimeter-Wave Imaging With Accelerated Super-Resolution Range Migration Algorithm 期刊论文  OAI收割
IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 2019, 卷号: 67, 期号: 11, 页码: 4610-4621
作者:  
Guo, Qijia;  Liang, Jie;  Chang, Tianying;  Cui, Hong-Liang
  |  收藏  |  浏览/下载:41/0  |  提交时间:2020/08/24
Ghost imaging based on Pearson correlation coefficients 期刊论文  OAI收割
CHINESE PHYSICS B, 2015, 卷号: 24, 期号: 5, 页码: 54203
作者:  
Yu Wen-Kai;  Yao Xu-Ri;  Liu Xue-Feng;  Li Long-Zhen;  Zhai Guang-Jie
收藏  |  浏览/下载:31/0  |  提交时间:2015/09/28
一种基于加权颜色聚合向量的图像检索方法 期刊论文  OAI收割
微电子学与计算机, 2014, 卷号: 31, 期号: 2, 页码: 31-34,38
徐朝辉; 樊银亭; 闫奎名; 滕东兴; 杨海燕
  |  收藏  |  浏览/下载:41/0  |  提交时间:2014/12/16
A method for obtaining scattering decomposed images in optical coherence tomography 期刊论文  OAI收割
j. opt., 2014, 卷号: 16, 期号: 7, 页码: 75302
作者:  
Guo, Xin;  Wang, Xiangzhao;  Bu, Peng;  Nan, Nan;  Li, Zhongliang
收藏  |  浏览/下载:34/0  |  提交时间:2016/11/28
A method for obtaining scattering decomposed images in optical coherence tomography 期刊论文  OAI收割
j. opt., 2014, 卷号: 16, 期号: 7, 页码: 75302
作者:  
Guo, Xin;  Wang, Xiangzhao;  Bu, Peng;  Nan, Nan;  Li, Zhongliang
收藏  |  浏览/下载:24/0  |  提交时间:2016/11/28
Astronomical image restoration through atmosphere turbulence by lucky imaging (EI CONFERENCE) 会议论文  OAI收割
3rd International Conference on Digital Image Processing, ICDIP 2011, April 15, 2011 - April 17, 2011, Chengdu, China
作者:  
收藏  |  浏览/下载:45/0  |  提交时间:2013/03/25
In this paper  we develop a lucky imaging system to restore astronomical images through atmosphere turbulence. Our system takes very short exposures  on the order of the atmospheric coherence time. The rapidly changing turbulence leads to a very variable point spread function (PSF)  and the variability of the PSF leads to some frames having better quality than the rest. Only the best frames are selected  aligned and co-added to give a final image with much improved angular resolution. Our system mainly consists of five parts: preprocessing  frame selection  image registration  image reconstruction  and image enhancement. Our lucky imaging system has been successfully applied to restore the astronomical images taken by a 1.23m telescope. We have got clear images of moon surface and Jupiter  and our system can be demonstrated to greatly improve the imaging resolution through atmospheric turbulence. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).  
Automatic bridge extraction for optical images (EI CONFERENCE) 会议论文  OAI收割
6th International Conference on Image and Graphics, ICIG 2011, August 12, 2011 - August 15, 2011, Hefei, Anhui, China
Gu D.-Y.; Zhu C.-F.; Shen H.; Hu J.-Z.; Chang H.-X.
收藏  |  浏览/下载:44/0  |  提交时间:2013/03/25
This paper describes a novel hierarchy algorithm for extracting bridges over water in optical images. To reduce the omission of bridges by searching the edge  we extract the river regions which the bridges are included in. Firstly  we segment the optical image to get the coarse water bodies using iterative threshold  eliminate the noise regions and add the missing regions based on k-means clustering with texture information and spatial coherence. Then  the blanks are connected based on shape features and candidate bridge regions are segmented from river regions. Finally  the bridges are verified by geometric information and the ubiety between bridges and river. The results show that this approach is efficient and effective for extracting bridges in satellite image from Google Earth and in aerial optical images acquired by unmanned aerial vehicle. 2011 IEEE.