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Enhanced Image Super-Resolution Technique Using Convolutional Neural Network

  • Kah Keong Chua
  • Yong Haur Tay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8237)

Abstract

A framework for image super resolution using Convolutional Neural Network (CNN) was developed. In this paper, we focus on verifying the performance of Convolutional Neural Network compared to other methods. CNN generally outperforms other super resolution methods. The training images were collected from various categories, i.e. flowers, buildings, animals, vehicles, human and cuisine. The neural network trained with multiple categories of training images made the CNN more robust towards different test scenarios. Common image degradation, i.e. motion blur and noise, can be reduced when the CNN is provided with proper training samples.

Keywords

Image Super Resolution Convolutional Neural Network motion blur image denoising 

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Kah Keong Chua
    • 1
  • Yong Haur Tay
    • 1
  1. 1.Centre for Computing and Intelligent SystemsUniversiti Tunku Abdul RahmanKuala LumpurMalaysia

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