방송공학회논문지 (Journal of Broadcast Engineering)

  • 제27권5호
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  • Pages.812-815
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  • 2022
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  • 1226-7953(pISSN)
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  • 2287-9137(eISSN)
한국방송∙미디어공학회 (The Korean Institute of Broadcast and Media Engineers)
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햇빛 아래에서 향상된 시인성을 위한 Piece-wise Linear Enhancement Curves 기반 영상 개선

Image Enhancement based on Piece-wise Linear Enhancement Curves for Improved Visibility under Sunlight

  • 이준민 (인하대학교 전기컴퓨터공학과) ;
  • 송병철 (인하대학교 전기컴퓨터공학과)
  • Lee, Junmin (Department of Electrical and Computer Engineering, Inha University) ;
  • Song, Byung Cheol (Department of Electrical and Computer Engineering, Inha University)
  • 투고 : 2022.07.25
  • 심사 : 2022.09.05
  • 발행 : 2022.09.30
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초록

햇빛 아래에서 디지털 기기에 표시되는 영상은 일반적으로 원본 영상보다 어둡게 인식되어 시인성이 저하된다. 더 나은 시인성을 위해서, 주변광에 적응적인 전역 휘도 보상 혹은 톤 매핑이 필요하다. 하지만 기존의 기법들은 색차 보상에 한계가 존재하고, 무거운 계산 비용 때문에 실제 환경에서 활용하는데 어려움이 존재한다. 이를 해결하기 위해 본 논문에서는 휘도와 색차를 모두 보상하는 Piece-wise Linear Enhancement Curves (PLECs) 기반 영상 개선 기법을 제안한다. 이때, PLECs은 딥러닝을 통해 회귀 되며, lookup table 형식으로 구현되어 실시간 동작이 가능하다. 실험 결과 제안 방법이 낮은 계산 비용으로 원본 영상 대비 더 나은 시인성을 가짐을 보인다

Images displayed on a digital devices under the sunlight are generally perceived to be darker than the original images, which leads to a decrease in visibility. For better visibility, global luminance compensation or tone mapping adaptive to ambient lighting is required. However, the existing methods have limitations in chrominance compensation and are difficult to use in real world due to their heavy computational cost. To solve these problems, this paper propose a piece-wise linear curves (PLECs)-based image enhancement method to improve both luminance and chrominance. At this time, PLECs are regressed through deep learning and implemented in the form of a lookup table to real-time operation. Experimental results show that the proposed method has better visibility compared to the original image with low computational cost.

키워드

과제정보

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)).

참고문헌

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