Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Deep Learning-based Super Resolution for Phase-only Holograms

위상 홀로그램을 위한 딥러닝 기반의 초고해상도

  • 김우석 (광운대학교 전자재료공학과) ;
  • 박병서 (광운대학교 전자재료공학과) ;
  • 김진겸 (광운대학교 전자재료공학과) ;
  • 오관정 (한국전자통신연구원) ;
  • 김진웅 (한국전자통신연구원) ;
  • 김동욱 (광운대학교 전자재료공학과) ;
  • 서영호 (광운대학교 전자재료공학과)
  • Received : 2020.10.05
  • Accepted : 2020.10.29
  • Published : 2020.11.30
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Abstract

In this paper, we propose a method using deep learning for high-resolution display of phase holograms. If a general interpolation method is used, the brightness of the reconstruction result is lowered, and noise and afterimages occur. To solve this problem, a hologram was trained with a neural network structure that showed good performance in the single-image super resolution (SISR). As a result, it was possible to improve the problem that occurred in the reconstruction result and increase the resolution. In addition, by adjusting the number of channels to increase performance, the result increased by more than 0.3dB in same training.

본 논문에서는 위상 홀로그램의 고해상도 디스플레이를 위하여 딥러닝을 사용하는 방법을 제안한다. 일반적인 보간법을 사용하면 복원결과의 밝기가 낮아지고 노이즈와 잔상이 생기는 문제점이 발생한다. 이를 해결하고자 SISR(Single-Image Super Resolution) 분야에서 좋은 성능을 보였던 신경망 구조로 홀로그램을 학습시켰다. 그 결과로 복원결과에서 발생한 문제를 개선하며 해상도를 증가시킬 수 있었다. 또한 성능을 높이기 위해 채널 수를 조절하여 동일한 학습 시에 0.3dB 이상의 결과 상승을 보였다.

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