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Accurate multi-image super-resolution using deep residual networks
Wangcai Zhao, Can Cui,
Jun Ke
*
, Xiaoli Long
*
*
此作品的通讯作者
光电学院
Beijing Institute of Technology
Guangzhou University
科研成果
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探究 'Accurate multi-image super-resolution using deep residual networks' 的科研主题。它们共同构成独一无二的指纹。
分类
加权
按字母排序
Computer Science
Low Resolution Image
100%
super resolution
100%
Deep Residual Network
100%
Structural Similarity
33%
Reconstruction Result
33%
peak signal to noise ratio
33%
Convolutional Neural Network
16%
Deep Convolutional Neural Networks
16%
Art Performance
16%
Convolutional Layer
16%
Performance Improvement
16%
Engineering
Low Resolution Image
100%
Single Image
66%
Signal-to-Noise Ratio
33%
Peak Signal
33%
Convolutional Neural Network
33%
Structural Similarity
33%
Scale Factor
16%
Performance Improvement
16%
Convolutional Layer
16%
Physics
Signal-to-Noise Ratio
100%
Convolutional Neural Network
100%
Earth and Planetary Sciences
State of the Art
100%