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Image Super-Resolution Reconstruction Based on Multi-scale Convolutional Neural Network

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Book cover Genetic and Evolutionary Computing (ICGEC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1107))

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Abstract

For image super-resolution based on convolutional neural network, there are many problems such as large amount of calculation, many parameters, and unresolved images. This paper proposes an image super-resolution reconstruction algorithm based on multi-scale convolutional neural network. The multi-scale convolution kernel method is introduced into convolutional neural networks. Multi-scale feature extraction is achieved for different sizes of convolutional layers, at the same time, the learning parameters are improved and the network parameters are reduced. Maxout is used as an activation function to introduce competing elements. At the same time, the Skip Connection in the residual network is added to the network model to accelerate the training of deep neural networks. Experiments show that the subjective visual and objective evaluation of this algorithm has been improved to a certain extent. The edge effect of the reconstructed high-resolution image is more clear when reducing the number of network parameters, and more detailed image information is recovered.

Research Project Supported by Shanxi Scholarship Council of China (NO. 2017-049).

Supported by State Key Laboratory of Air Traffic Management System and Technology, (NO. SKLATM201803).

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Authors and Affiliations

  1. College of Information and Computer Science, Taiyuan University of Technology, Taiyuan, Shanxi, China

    Jianqiao Song & Feng Wang

Authors
  1. Jianqiao Song
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  2. Feng Wang
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Corresponding author

Correspondence to Feng Wang .

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Editors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Jeng-Shyang Pan

  2. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  3. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China

    Yongquan Liang

  4. School of Computer Science, Engineering and Mathematics, Flinders University, Bedford Park, Australia

    Shu-Chuan Chu

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Song, J., Wang, F. (2020). Image Super-Resolution Reconstruction Based on Multi-scale Convolutional Neural Network. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_42

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