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

Author

Listed:
  • Qiang Yu

    (College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
    School of Aeronautics & Astronautics, Sichuan University, Chengdu 610064, China)

  • Feiqiang Liu

    (College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China
    School of Aeronautics & Astronautics, 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
    School of Aeronautics & Astronautics, Sichuan University, Chengdu 610064, China)

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.

Suggested Citation

  • Qiang Yu & Feiqiang Liu & Long Xiao & Zitao Liu & Xiaomin Yang, 2021. "Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network," IJERPH, MDPI, vol. 18(11), pages 1-16, May.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:11:p:5890-:d:565842
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    References listed on IDEAS

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    1. Jiangmei Xiong & Yulin Hswen & John A. Naslund, 2020. "Digital Surveillance for Monitoring Environmental Health Threats: A Case Study Capturing Public Opinion from Twitter about the 2019 Chennai Water Crisis," IJERPH, MDPI, vol. 17(14), pages 1-15, July.
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    Cited by:

    1. Gwanggil Jeon & Abdellah Chehri, 2021. "Computing Techniques for Environmental Research and Public Health," IJERPH, MDPI, vol. 18(18), pages 1-4, September.

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