scholarly journals Optimized highway deep learning network for fast single image super-resolution reconstruction

2020 ◽  
Vol 17 (6) ◽  
pp. 1961-1970
Author(s):  
Viet Khanh Ha ◽  
Jinchang Ren ◽  
Xinying Xu ◽  
Wenzhi Liao ◽  
Sophia Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Single Image ◽  
Learning Network ◽  
Image Super Resolution ◽  
Deep Learning Network ◽  
Single Image Super Resolution

scholarly journals Single image super-resolution via Image Quality Assessment-Guided Deep Learning Network

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0241313
Author(s):  
Zhengqiang Xiong ◽  
Manhui Lin ◽  
Zhen Lin ◽  
Tao Sun ◽  
Guangyi Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Super Resolution ◽  
Single Image ◽  
Learning Network ◽  
Image Super Resolution ◽  
Deep Learning Network ◽  
Single Image Super Resolution

scholarly journals Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network

2021 ◽  
Vol 18 (11) ◽  
pp. 5890
Author(s):  
Qiang Yu ◽  
Feiqiang Liu ◽  
Long Xiao ◽  
Zitao Liu ◽  
Xiaomin Yang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Super Resolution ◽  
Model Complexity ◽  
Practical Application ◽  
Single Image ◽  
Feature Maps ◽  
Benchmark Datasets ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.

scholarly journals Deep Learning Based Single Image Super-resolution: A Survey

2019 ◽  
Vol 16 (4) ◽  
pp. 413-426 ◽  
Author(s):  
Viet Khanh Ha ◽  
Jin-Chang Ren ◽  
Xin-Ying Xu ◽  
Sophia Zhao ◽  
Gang Xie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Low-light-level image super-resolution reconstruction via deep learning network

2021 ◽  
Author(s):  
Bowen Wang ◽  
Yan Zou ◽  
Yuhai Li ◽  
WenLin Lu ◽  
Chao Zuo
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Light Level ◽  
Low Light ◽  
Learning Network ◽  
Image Super Resolution ◽  
Deep Learning Network
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