Multi-Perspective Discriminators-Based Generative Adversarial Network for Image Super Resolution

Oh Young Lee, Yoon Ho Shin, Jong Ok Kim

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Recently, generative adversarial network-based image super resolution has been investigated, and it has been shown to lead to overwhelming improvements in subjective quality. However, it also leads to checkerboard artifacts and the unpleasing high-frequency (HF) components. In this paper, we propose a multi-discriminators-based image super resolution method that distinguishes those artifacts from various perspectives. First, the DCT perspective discriminator is proposed because the checkerboard artifacts are easily separated on the frequency domain. Second, the gradient perspective discriminator is proposed, because the unpleasing HF components can be discriminated on the gradient magnitude distribution. These proposed multi-perspective discriminators can easily identify artifacts, and they can help the generator reproduce artifact-less SR images. The experimental results show that the proposed SR-GAN with multi-perspective discriminators achieves objective and subjective quality improvements in terms of PSNR, SSIM, PI and MOS, as compared to the conventional SR-GAN by reducing the aforementioned artifacts.

Original languageEnglish
Article number8845591
Pages (from-to)136496-136510
Number of pages15
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019 Jan 1

Keywords

  • deep learning for super resolution
  • Image super-resolution
  • multi-discriminators
  • SR GAN

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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