Deep residual networks with a fully connected reconstruction layer for single image super-resolution

Tang Yongliang; Huang Jiashui; Zhang Faen; Gong Weiguo

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Deep residual networks with a fully connected reconstruction layer for single image super-resolution

机译：具有完全连接的重建层的深度剩余网络，用于单图像超分辨率

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来源
《Neurocomputing》 |2020年第10期|186-199|共14页
作者
Tang Yongliang; Huang Jiashui; Zhang Faen; Gong Weiguo;
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作者单位

Innovat Co Ltd Beijing 100089 Peoples R China;

Innovat Co Ltd Beijing 100089 Peoples R China;

Innovat Co Ltd Beijing 100089 Peoples R China;

Chongqing Univ Key Lab Optoelect Technol & Syst Educ Minist Chongqing 400044 Peoples R China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
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1. Super-resolution Reconstruction Using Multiconnection Deep Residual Network Combined an Improved Loss Function for Single-frame Image [J] . Yuhua Peng Multimedia Tools and Applications . 2020,第13a14期

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2. Super-resolution reconstruction of single anisotropic 3D MR images using residual convolutional neural network [J] . Du Jinglong, He Zhongshi, Wang Lulu, Neurocomputing . 2020,第Juna7期

机译：使用剩余卷积神经网络超级分辨率重建单个各向异性三维MR图像
3. Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single-Image Super-Resolution [J] . Tang Yongliang, Gong Weiguo, Chen Xi, Neural Networks and Learning Systems, IEEE Transactions on . 2020,第5期

机译：深度成立 - 残余拉普拉斯金字塔网络，用于精确单图像超分辨率
4. Single Image Super-resolution Reconstruction with Wavelet based Deep Residual Learning [C] . Jianfang Dou, Zimei Tu, Xishuai Peng Chinese Control and Decision Conference . 2020

机译：基于小波的深度残差学习的单图像超分辨率重建
5. CNN-Based Single Image Super-Resolution Network and Biomedical Image Applications [D] . Bayram, Samet. 2018

机译：基于CNN的单图像超分辨率网络和生物医学图像应用
6. Deeply Recursive Low- and High-Frequency Fusing Networks for Single Image Super-Resolution [O] . Cheng Yang, Guanming Lu 2020

机译：深度递归低和高频熔断网络用于单图像超分辨率
7. Single-frame super-resolution for remote sensing images based on improved deep recursive residual network [O] . Jiali Tang, Jie Zhang, Dan Chen, 2021

机译：基于改进的深递归残差网络的遥感图像的单帧超分辨率

Deep residual networks with a fully connected reconstruction layer for single image super-resolution

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