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浏览/检索结果: 共7条，第1-7条 帮助 限定条件 专题：兰州理工大学    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Inverted N-Type Lightweight Network Based on Back Projection for Image Super-Resolution Reconstruction 期刊论文 Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics, 2022, 卷号: 34, 期号: 6, 页码: 923-932 作者:  Song, Zhaoyang;  Zhao, Xiaoqiang;  Hui, Yongyong;  Jiang, Hongmei 收藏  |  浏览/下载：11/0  |  提交时间：2022/07/20 Deep learning  Image reconstruction  Optical resolving power  Signal to noise ratio  Backprojections  Deep learning  Image super-resolution reconstruction  Inverted N-type network  Low resolution images  Network-based  Reconstruction algorithms  Resolution images  Super-resolution reconstruction  Superresolution   Fusing Attention Network based on Dilated Convolution for Super Resolution 期刊论文 IEEE Transactions on Cognitive and Developmental Systems, 2022 作者:  Song, Zhaoyang;  Zhao, Xiaoqiang;  Hui, Yongyong;  Jiang, Hongmei 收藏  |  浏览/下载：9/0  |  提交时间：2022/07/20 Convolution  Data mining  Filtration  Image reconstruction  Locks (fasteners)  Optical resolving power  Quality control  Dilated convolutional attention module.  Features extraction  High-frequency informations  Images reconstruction  Information filter  Multi feature attention block  Multifeatures  Single super resolution  Superresolution   Gradual deep residual network for super-resolution 期刊论文 Multimedia Tools and Applications, 2021, 卷号: 80, 期号: 7, 页码: 9765-9778 作者:  Song, Zhaoyang;  Zhao, Xiaoqiang;  Jiang, Hongmei 收藏  |  浏览/下载：2/0  |  提交时间：2021/06/03 Deep neural networks  Image enhancement  Optical resolving power  Benchmark datasets  High resolution image  Low resolution images  Quantitative evaluation  Reconstruction networks  Reconstruction process  State-of-the-art methods  Visual evaluation   Mural Image Super Resolution Reconstruction Based on Multi-Scale Residual Attention Network 期刊论文 LASER & OPTOELECTRONICS PROGRESS, 2020, 卷号: 57, 期号: 16 作者:  Xu Zhigang;  Yan Juanjuan;  Zhu Honglei 收藏  |  浏览/下载：18/0  |  提交时间：2022/03/01 super-resolution  mural image  residual network  attention mechanism  multi-scale feature   Super-Resolution Reconstruction of Deep Residual Network with Multi-Level Skip Connections 期刊论文 Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 2019, 卷号: 41, 期号: 10, 页码: 2501-2508 作者:  Zhao, Xiaoqiang;  Song, Zhaoyang 收藏  |  浏览/下载：18/0  |  提交时间：2020/11/14 Convolution  Convolutional neural networks  Eigenvalues and eigenfunctions  Gradient methods  Image processing  Optical resolving power  Stochastic models  Stochastic systems  Evaluation index  Feature information  High resolution image  Image information  Low resolution images  Multilevels  Stochastic gradient descent  Super resolution reconstruction   Deep CNN jointing low-high level feature for image super-resolution 会议论文 Chengdu, China, December 12, 2018 - December 14, 2018 作者:  Song, Xuhui;  Liu, Weirong;  Liu, Jie;  Liu, Chaorong;  Lu, Chunyan 收藏  |  浏览/下载：12/0  |  提交时间：2020/11/15 Image reconstruction  Neural networks  Optical resolving power  Semantics  Textures  Convolutional neural network  High resolution image  High-level semantic features  Image super resolutions  Low resolution images  Low-high  Perceptual effects  Residual learning   Deep CNN Jointing Low-High Level Feature for Image Super-Resolution 会议论文 作者:  Song, Xuhui;  Liu, Weirong;  Liu, Jie;  Liu, Chaorong;  Lu, Chuyan 收藏  |  浏览/下载：0/0  |  提交时间：2019/11/15 jointing low-high level feature  CNN  residual learning  image super-resolution