Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Algorithm for Improving Visibility under Ambient Lighting Using Deep Learning

딥러닝을 이용한 외부 조도 아래에서의 시인성 향상 알고리즘

  • Lee, Hee Jin (Department of Electrical and Computer Engineering, Inha University) ;
  • Song, Byung Cheol (Department of Electrical and Computer Engineering, Inha University)
  • 이희진 (인하대학교 전기컴퓨터공학과) ;
  • 송병철 (인하대학교 전기컴퓨터공학과)
  • Received : 2022.07.25
  • Accepted : 2022.09.08
  • Published : 2022.09.30
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Abstract

Display under strong ambient lighting is perceived darker than it really is. Existing techniques for solving the problem in terms of software show limitations in that image enhancement techniques are applied regardless of ambient lighting or chrominance is not improved compared to luminance. Therefore, this paper proposes a visibility enhancement algorithm using deep learning to adaptively respond to ambient lighting values and an equation to restore optimal chrominance for luminance. The algorithm receives an ambient lighting value with the input image, and then applies a deep learning model and chrominance restoration equation to generate an image to minimize the difference between the degradation modeling of enhanced image and the input image. Qualitative evaluation proves that the algorithm shows excellent performance in improving visibility under strong ambient lighting through comparison of images applied with degradation modeling.

강한 외부 조도 아래에서의 디스플레이는 인간의 인지 시스템에 의해, 실제보다 더 어둡게 인지된다. 해당 문제를 소프트웨어 측면에서 해결하기 위한 기존의 기법들은, 외부 조도에 대응하지 못하거나 밝기에 비해 색상이 향상되지 못하는 한계를 보인다. 따라서 본 논문은 외부 조도 값에 따라 영상의 밝기 및 색상을 향상하는 시인성 개선 알고리즘을 제안한다. 해당 알고리즘은 입력 영상과 함께 외부 조도 값을 인자로 받은 후, 딥러닝 모델을 통한 luminance 학습 및 chrominance 복원 방정식을 적용하여, 개선된 영상의 열화 현상과 입력 영상과의 대비 차이가 최소화되도록 영상을 생성한다. 이는 정성적 평가에서 열화 모델링 적용 영상 비교를 통해 해당 알고리즘이 강한 외부 조도 아래에서의 시인성 개선에 뛰어난 성능을 보임을 확인할 수 있다.

Keywords

Acknowledgement

This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2021-0-02052) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation), and was supported by Institute of Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government (MSIT) (2020-0-01389, Artificial Intelligence Convergence Research Center(Inha University)).

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