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Deep Residual Neural Network Design for Super-Resolution Imaging

  • Wei-Ting ChenEmail author
  • Pei-Yin ChenEmail author
  • Bo-Chen LinEmail author
Conference paper
  • 522 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1013)

Abstract

Convolution neural network recently confirmed the high-quality reconstruction for single-image super-resolution (SR). In this paper we present a Deep Level Residual Network (DLNR), a low-memory effective neural network to reconstruct super-resolution images. This neural network also has the following characteristics. (1) Ability to perform different convolution size operations on the image which can achieve more comprehensive feature extraction effects. (2) Using residual learning to expand the depth of the network and increase the capacity of learning. (3) Taking the skill of parameter sharing between the network module to reduce the number of parameters. After the experiment, we find that DLNR can achieve 37.78 in PSNR and 0.975 in SSIM when using Manga109 as testing set for 2× SR.

Keywords

Super-resolution Neural network Residual learning PSNR SSIM 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityTainanTaiwan, R.O.C.

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