Journal of Broadcast Engineering (방송공학회논문지)

The Korean Institute of Broadcast and Media Engineers (한국방송∙미디어공학회)
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Color Noise Detection and Image Restoration for Low Illumination Environment

저조도 환경 기반 색상 잡음 검출 및 영상 복원

  • Oh, Gyoheak (Department of Electrical and Computer Engineering) ;
  • Lee, Jaelin (Department of Electrical and Computer Engineering) ;
  • Jeon, Byeungwoo (Department of Electrical and Computer Engineering)
  • 오교혁 (성균관대학교 전자전기컴퓨터공학과) ;
  • 이재린 (성균관대학교 전자전기컴퓨터공학과) ;
  • 전병우 (성균관대학교 전자전기컴퓨터공학과)
  • Received : 2020.10.21
  • Accepted : 2021.01.06
  • Published : 2021.01.30
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Abstract

Recently, the crime prevention and culprit identification even in a low illuminated environment by CCTV is becoming ever more important. In a low lighting situation, CCTV applications capture images under infrared lighting since it is unobtrusive to human eye. Although the infrared lighting leads to advantage of capturing an image with abundant fine texture information, it is hard to capture the color information which is very essential in identifying certain objects or persons in CCTV images. In this paper, we propose a method to acquire color information through DCGAN from an image captured by CCTV in a low lighting environment with infrared lighting and a method to remove color noise in the acquired color image.

CCTV를 사용하여 저조도와 같은 열악한 환경에서도 범죄 예방 및 특정 대상을 정확히 확인하는 것이 최근 더욱 중요해지고 있다. 저조도 환경하의 CCTV 응용에서는 눈에 거슬리지 않는 근적외선 조명을 이용하여 영상을 획득하는데, 이 경우, 비록 사람 눈에는 어두운 저조도 환경이지만 근적외선 조명을 사용하기 때문에 영상의 상세 텍스처 정보를 얻을 수 있는 장점은 있지만, CCTV 영상내의 물체 판별이나 인물 확인을 위하여 매우 요긴한 정보인 색상 정보는 얻기 힘들다는 단점이 있다. 본 논문에서는 저조도 환경에서 근적외선 조명을 사용하여 얻은 CCTV 영상으로부터 DCGAN을 사용하여 색상정보를 획득하는 방법과 이때 재구성된 색상 영상에 생기는 색상 잡음을 제거하는 방법을 제시한다.

Keywords

Acknowledgement

이 논문은 2018년도 정부(과학기술정보통신부)의 재원으로 정보통신기술진흥센터의 지원을 받아 수행된 연구임 (NO. 2018-0-00348, CCTV 제약점 개선을 통해 범인 검거율 저하 문제 해결을 지원하는 지능형 영상 보안 시스템 기술 개발). 연구 데이터 수집에 도움을 준 이준형 연구원에게 감사를 드립니다.

References

  1. X. Guo, Y. Li, and H. Ling, "LIME: Low-Light Image Enhancement via Illumination Map Estimation," IEEE Transactions on Image Processing, Vol. 26, No. 2, pp. 982-993, Feb, 2017. https://doi.org/10.1109/TIP.2016.2639450
  2. W. Wang, C. Wei, W. Yang, J. Liu, "GLADNet: Low-Light Enhancement Network with Global Awareness," IEEE international Conference on Automatic Face & Gesture Recognition, Xi'an, China, pp. 751-755, May, 2018.
  3. Y. Jiang, X. Gong, D. Liu, Y. Cheng, C. Fang, X. Shen, J. Yang, P. Zhou, and Z. Wang, "EnlightenGAN: Deep Light Enhancement without Paired Supervision," arXiv preprint arXiv:1906.06972, 2019.
  4. A. Radford, and L. Metz, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks," International Conference on Learning Representations, Caribe Hilton, Puerto Rico, pp. 1-16, 2016.
  5. I. Goodfellow, J. P. Abadie, M. Mirza, B. Xu, D. W. Farley, S. Ozair, A. Courvile, Y. Bengio, "Generative Adversarial Networks," Proceedings of the International Conference on Neural Information Processing Systems, Montreal, Canada, pp. 2672-2680, 2014.
  6. O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," International Conference on Medical Computing and Computer-Assisted Intervention, Munich, Germany, pp. 234-241, 2015.
  7. T. Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature Pyramid Networks for Object Detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, Hawaii, pp. 2117-2125, 2017.
  8. G. Huang, Z. Liu, and L. Maaten, "Densely Connected Convolutional Networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, Hawaii, pp. 4700-4708,2017.
  9. K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask R-CNN," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, Hawaii, pp. 2961-2969,2017.
  10. G. Oh, J. Lee, and B. Jeon, "Noise Removal in Reconstructed Color Image by GAN for Low Light CCTV Application," International conference on Electronics, information, and Communication 2020, Barcelona, Spain, 2020.