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

Qiang Yu, Feiqiang Liu, Long Xiao, Zitao Liu and Xiaomin Yang
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Qiang Yu: College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
Feiqiang Liu: College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
Long Xiao: Science and Technology on Electronic Information Control Laboratory, Chengdu 610036, China
Zitao Liu: TAL Education Group, Beijing 100080, China
Xiaomin Yang: College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China

IJERPH, 2021, vol. 18, issue 11, 1-16

Abstract: 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.

Keywords: image super-resolution; real-time; deep learning; lightweight model; environment research; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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