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Super resolution reconstruction algorithm of video image based on deep self encoding learning

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Abstract

Super resolution reconstruction of video image is a research hotspot in the field of image processing, and it is widely used in video surveillance, image processing, criminal analysis and other fields. Super resolution image reconstruction can reconstruct a high resolution image from low resolution images, and this technology has become a research hotspot in the field of image processing. In recent years, deep learning has been developed rapidly in the field of multimedia processing, and image super resolution restoration technology based on deep learning has gradually become the mainstream technology. In view of the existing image super-resolution algorithm problems, such as more parameters, larger amount of calculation, longer training time, blurred image texture, we use the deep self-coding learning method to improve it. We analyze the advantages and disadvantages of the existing technology from the network type, network structure, training methods and so on, and sort out the development of the technology. The experimental results show that the improved network model achieves better super-resolution results, and the subjective visual effect and objective evaluation index are improved obviously, and the image sharpness and edge sharpness are improved obviously.

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Authors and Affiliations

  1. Engineering and IT, University of Technology Sydney, PO Box 123, Broadway, Sydney, NSW, 2007, Australia

    Shang Xi

  2. Shanghai Key Lab of Modern Optical Systems, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Chunxue Wu & Linhua Jiang

Authors
  1. Shang Xi
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  2. Chunxue Wu
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  3. Linhua Jiang
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Correspondence to Linhua Jiang.

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Xi, S., Wu, C. & Jiang, L. Super resolution reconstruction algorithm of video image based on deep self encoding learning. Multimed Tools Appl 78, 4545–4562 (2019). https://doi.org/10.1007/s11042-018-6062-x

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  • DOI: https://doi.org/10.1007/s11042-018-6062-x

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