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检索条件"机构=The Key Laboratory of Technology in Geo-Spatial Information Processing and Application System"
589 条 记 录,以下是191-200 订阅
排序:
Target Amplitude Characteristic Analysis With Neighborhood information Using C-band Circular SAR Data
Target Amplitude Characteristic Analysis With Neighborhood I...
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IEEE International Conference on Radar
作者: Xiaoyang Yue Fei Teng Yun Lin Wen Hong Key Laboratory of Spatial Information Processing and Application System Technology Areospace Information Research Institute Chinese Academy of Sciences Beijing China School of Electronic Information Engineering North China University of Technology Beijing China
The circular synthetic aperture radar (SAR) can observe the experimental scene from all angles. The backscatter intensity of the target in the scene can be obtained. Different targets in the imaging scene show differe... 详细信息
来源: 评论
The Z-eigenpairs of orthogonally diagonalizable symmetric tensors
arXiv
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arXiv 2021年
作者: Wang, Lei Geng, Xiurui Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of the Chinese Academy of Sciences Beijing100049 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Science Beijing100190 China
In this paper, we focus on a special class of symmetric tensors, which can be orthogonally diagonalizable, and investigate their Z-eigenpairs problem. We show that the eigenpairs can be uniformly expressed using sever... 详细信息
来源: 评论
Article dgfnet: Dual gate fusion network for land cover classification in very high-resolution images
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Remote Sensing 2021年 第18期13卷
作者: Guo, Yongjie Wang, Feng Xiang, Yuming You, Hongjian The Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China
Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on land cover classification thanks to their outstanding nonlinear feature extraction abil-ity. DCNNs are usually desig... 详细信息
来源: 评论
FAN: Frequency aggregation network for real image super-resolution
arXiv
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arXiv 2020年
作者: Pang, Yingxue Li, Xin Jin, Xin Wu, Yaojun Liu, Jianzhao Liu, Sen Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China
Single image super-resolution (SISR) aims to recover the high-resolution (HR) image from its low-resolution (LR) input image. With the development of deep learning, SISR has achieved great progress. However, It is sti... 详细信息
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Deep local and global spatiotemporal feature aggregation for blind video quality assessment
arXiv
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arXiv 2020年
作者: Zhou, Wei Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China
In recent years, deep learning has achieved promising success for multimedia quality assessment, especially for image quality assessment (IQA). However, since there exist more complex temporal characteristics in video... 详细信息
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Learning omni-frequency region-adaptive representations for real image super-resolution
arXiv
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arXiv 2020年
作者: Li, Xin Jin, Xin Yu, Tao Pang, Yingxue Sun, Simeng Zhang, Zhizheng Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China
Traditional single image super-resolution (SISR) methods that focus on solving single and uniform degradation (i.e., bicubic down-sampling), typically suffer from poor performance when applied into real-world low-reso... 详细信息
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Deep multi-scale features learning for distorted image quality assessment
arXiv
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arXiv 2020年
作者: Zhou, Wei Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China
Image quality assessment (IQA) aims to estimate human perception based image visual quality. Although existing deep neural networks (DNNs) have shown significant effectiveness for tackling the IQA problem, it still ne... 详细信息
来源: 评论
Towards Semantically Scalable Image Coding using Semantic Map
Towards Semantically Scalable Image Coding using Semantic Ma...
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IEEE International Symposium on Circuits and systems (ISCAS)
作者: Ning Yan Dong Liu Houqiang Li Feng Wu Zhiwei Xiong Zheng-Jun Zha CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei 230027 China
We propose an image coding scheme that compresses image into semantically scalable bitstream using deep neural networks. This scheme is expected to support intelligent analysis when the bitstream is partially decoded,... 详细信息
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LIRA: Lifelong image restoration from unknown blended distortions
arXiv
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arXiv 2020年
作者: Liu, Jianzhao Lin, Jianxin Li, Xin Zhou, Wei Liu, Sen Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China
Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task. To alleviate this problem, we raise t... 详细信息
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Blind omnidirectional image quality assessment with viewport oriented graph convolutional networks
arXiv
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arXiv 2020年
作者: Xu, Jiahua Zhou, Wei Chen, Zhibo CAS Key Laboratory of Technology Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China
Quality assessment of omnidirectional images has become increasingly urgent due to the rapid growth of virtual reality applications. Different from traditional 2D images and videos, omnidirectional contents can provid... 详细信息
来源: 评论