FALL 2022 IYRC
  • FRONT
  • PAST CONFERENCES
    • IYRC Fall 2022 >
      • Authors
    • IYRC Fall 2021 >
      • Authors
    • IYRC Spring 2021 >
      • Authors
    • IYRC 2020 >
      • Authors
    • IYRC 2019
    • IYRC 2018 PROCEEDING
  • PAST SUMMER PROGRAMS
    • IYRC Summer 2022
    • IYRC Summer 2021
  • IYRC HOME PAGE

Improving image quality using deep learning based super resolution

KIM, Dohyun
DOI: http://doi.org/10.34614/2022IYRC29
Category: STEM
Abstract – Super resolution is one of the important computer vision tasks. A low-definition image can be changed to the high-definition image through super resolution. Likewise, it can be applied to a video. Especially, the popularization of smart devices and the inundation of video-based contents cause a gradual increase in the importance of super resolution tasks. However, the super resolution task is an ill-posed problem without only one correct answer. The reason is that given a low-definition image, there is no only one answer sheet corresponding to the high-definition version of the corresponding image with possible multiple answer sheets. Namely, super resolution is the technology greatly contributing to the society but has an ill-posed problem. Hence, super resolution is a very invaluable subject from the research perspective. The study performs the recurrence of the existing methodology for super resolution and presents the new deep learning model called the Boosted Super Resolution Generative Adversarial Nets (BSRGAN) by improving the methodology.
​
  • PAPER
  • PRESENTATION VIDEO
<
>
Download PDF
  • FRONT
  • PAST CONFERENCES
    • IYRC Fall 2022 >
      • Authors
    • IYRC Fall 2021 >
      • Authors
    • IYRC Spring 2021 >
      • Authors
    • IYRC 2020 >
      • Authors
    • IYRC 2019
    • IYRC 2018 PROCEEDING
  • PAST SUMMER PROGRAMS
    • IYRC Summer 2022
    • IYRC Summer 2021
  • IYRC HOME PAGE