scholarly journals Single image super-resolution by residual recovery based on an independent deep convolutional network

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Fei Wang ◽  
Mali Gong
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Convolutional Network ◽  
Image Super Resolution ◽  
Deep Convolutional Network ◽  
Single Image Super Resolution

Lightweight group convolutional network for single image super-resolution

2020 ◽  
Vol 516 ◽  
pp. 220-233 ◽  
Author(s):  
Aiping Yang ◽  
Bingwang Yang ◽  
Zhong Ji ◽  
Yanwei Pang ◽  
Ling Shao
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Convolutional Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution

scholarly journals Single Image Super-Resolution Based on Global Dense Feature Fusion Convolutional Network

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 316 ◽  
Author(s):  
Wang Xu ◽  
Renwen Chen ◽  
Bin Huang ◽  
Xiang Zhang ◽  
Chuan Liu
Keyword(s):  
Feature Fusion ◽  
Super Resolution ◽  
Great Success ◽  
Single Image ◽  
Global Features ◽  
Convolutional Network ◽  
Memory Mechanism ◽  
Basic Module ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Deep neural networks (DNNs) have been widely adopted in single image super-resolution (SISR) recently with great success. As a network goes deeper, intermediate features become hierarchical. However, most SISR methods based on DNNs do not make full use of the hierarchical features. The features cannot be read directly by the subsequent layers, therefore, the previous hierarchical information has little influence on the subsequent layer output, and the performance is relatively poor. To address this issue, a novel global dense feature fusion convolutional network (DFFNet) is proposed, which can take full advantage of global intermediate features. Especially, a feature fusion block (FFblock) is introduced as the basic module. Each block can directly read raw global features from previous ones and then learns the feature spatial correlation and channel correlation between features in a holistic way, leading to a continuous global information memory mechanism. Experiments on the benchmark tests show that the proposed method DFFNet achieves favorable performance against the state-of-art methods.

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