Deep learning-based image super-resolution considering quantitative and perceptual quality

Jun Ho Choi, Jun Hyuk Kim, Manri Cheon, Jong Seok Lee

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Recently, it has been shown that in super-resolution, there exists a tradeoff relationship between the quantitative and perceptual quality of super-resolved images, which correspond to the similarity to the ground-truth images and the naturalness, respectively. In this paper, we propose a novel super-resolution method that can improve the perceptual quality of the upscaled images while preserving the conventional quantitative performance. The proposed method employs a deep network for multi-pass upscaling in company with a discriminator network and two qualitative score predictor networks. Experimental results demonstrate that the proposed method achieves a good balance of the quantitative and perceptual quality, showing more satisfactory results than existing methods.

Original languageEnglish
Pages (from-to)347-359
Number of pages13
JournalNeurocomputing
Volume398
DOIs
Publication statusPublished - 2020 Jul 20

Bibliographical note

Funding Information:
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the “ICT Consilience Creative Program” (IITP-2019-2017-0-01015) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation). In addition, this work was also supported by the IITP grant funded by the Korea government (MSIT) ( R7124-16-0004 , Development of Intelligent Interaction Technology Based on Context Awareness and Human Intention Understanding).

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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