Image super-resolution model using an improved deep learning-based facial expression analysis

2020 ◽  
Author(s):  
Pyoung Won Kim
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Expression Analysis ◽  
Super Resolution ◽  
Facial Expression Analysis ◽  
Resolution Model ◽  
Image Super Resolution

scholarly journals Towards a fully automated surveillance of well-being status in laboratory mice using deep learning: Starting with facial expression analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. e0228059 ◽  
Author(s):  
Niek Andresen ◽  
Manuel Wöllhaf ◽  
Katharina Hohlbaum ◽  
Lars Lewejohann ◽  
Olaf Hellwich ◽  
...  
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Expression Analysis ◽  
Well Being ◽  
Laboratory Mice ◽  
Facial Expression Analysis ◽  
Automated Surveillance

Facial Expression Analysis Based on Synergetic Trust Model in Proactive Environment

2012 ◽  
Vol 23 (1) ◽  
pp. 21-31
Author(s):  
Chao XU ◽  
Zhi-Yong FENG ◽  
Jia-Fang WANG
Keyword(s):  
Facial Expression ◽  
Expression Analysis ◽  
Trust Model ◽  
Facial Expression Analysis

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.

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