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Single Image Super-Resolution with Vision Loss Function

  • Yi-Zhen Song
  • Wen-Yen Liu
  • Ju-Chin ChenEmail author
  • Kawuu W. Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11432)

Abstract

Super-resolution is the use of low-resolution images to reconstruct corresponding high-resolution images. This technology is used in many places such as medical fields and monitor systems. The traditional method is to interpolate to fill in the information lost when the image is enlarged. The initial use of deep learning is SRCNN, which is divided into three steps, extracting image block features, feature nonlinear mapping and reconstruction. Both PSNR and SSIM have significant progress compared with traditional methods, but there are still some details in detail restoration. defect. SRGAN will generate anti-network applications to SR problems. The method is to improve the image magnification by more than 4 times, which is easy to produce too smooth. In this study, we hope to improve the EnhanceNet by training with different loss functions and different types of images to achieve better reconstruction results.

Keywords

Super-resolution Deep learning Generative adversarial network 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yi-Zhen Song
    • 1
  • Wen-Yen Liu
    • 1
  • Ju-Chin Chen
    • 1
    Email author
  • Kawuu W. Lin
    • 1
  1. 1.National Kaohsiung University of Science TechnologyKaohsiungRepublic of China

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