Journal of Internet of Things and Convergence (사물인터넷융복합논문지)

The Korea Internet of Things Society (한국사물인터넷학회)
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Adaptive Enhancement of Low-light Video Images Algorithm Based on Visual Perception

시각 감지 기반의 저조도 영상 이미지 적응 보상 증진 알고리즘

  • Li Yuan (Division of Information and Communication Convergence Engineering, Mokwon University) ;
  • Byung-Won Min (Division of Information and Communication Convergence Engineering, Mokwon University)
  • 이원 (목원대학교 정보통신용합공학부) ;
  • 민병원 (목원대학교 정보통신용합공학부)
  • Received : 2024.02.23
  • Accepted : 2024.03.29
  • Published : 2024.04.30
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Abstract

Aiming at the problem of low contrast and difficult to recognize video images in low-light environment, we propose an adaptive contrast compensation enhancement algorithm based on human visual perception. First of all, the video image characteristic factors in low-light environment are extracted: AL (average luminance), ABWF (average bandwidth factor), and the mathematical model of human visual CRC(contrast resolution compensation) is established according to the difference of the original image's grayscale/chromaticity level, and the proportion of the three primary colors of the true color is compensated by the integral, respectively. Then, when the degree of compensation is lower than the bright vision precisely distinguishable difference, the compensation threshold is set to linearly compensate the bright vision to the full bandwidth. Finally, the automatic optimization model of the compensation ratio coefficient is established by combining the subjective image quality evaluation and the image characteristic factor. The experimental test results show that the video image adaptive enhancement algorithm has good enhancement effect, good real-time performance, can effectively mine the dark vision information, and can be widely used in different scenes.

저조도 환경에서 영상 이미지의 콘트라스트가 낮고 식별이 어려운 문제를 목표로 사람의 시각 감지 기반의 콘트라스트 적응 보상 증진 알고리즘을 제안한다. 첫째, 저조도 환경에서 평균 밝기, 평균 대역폭 요인의 영상 이미지 특징 요인을 추출하고, 원본 영상의 회색/색도 차이에 따라 사람의 시각적 콘트라스트 해상도 보상의 수학적 모델을 설정하며, 실제 컬러의 3원색에 대해 각각 비례 적분하여 보상한다. 다음으로 보상 정도가 명시각 차이를 적절하게 구별할 수 있는 것보다 낮을 때 보상 임계값 선형 보상이 명시각에서 전체 대역폭으로 설정된다. 마지막으로 주관적인 이미지 품질 평가와 이미지 특성 요인을 결합하여 비례 계수를 보상하는 자동 최적화 모델을 구축한다. 실험 테스트 결과는 영상 이미지 적응 증진 알고리즘이 우수한 증진 효과와 우수한 실시간 성능을 가지며 다크 비전 정보를 효과적으로 마이닝할 수 있으며 다양한 시나리오에서 널리 사용될 수 있음을 보여준다.

Keywords

References

  1. Z.Zhang, H.Zheng, R.Hong, M.L.Xu, S.C.Yan and M.Wang, "Deep Color Consistent Network for Low-Light Image Enhancement," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.1899-1908, 2022.
  2. H.D.Cheng and X.J.Shi, "A simple and effective histogram equalization approach to image enhancement," Digital Signal Processing, Vol.14, No.2, pp.158-170, 2004. https://doi.org/10.1016/j.dsp.2003.07.002
  3. E.H.Lang and J.J.Mccann. "Lightness and retinex theory," Journal of The optical scriety of America, Vol.61, No.1, pp.1-11, 1971. https://doi.org/10.1364/JOSA.61.000001
  4. P.P.Panik, R.Saha and K.Kim. "Contrast enhancement of low-light image using histogram equalization and illumination adjustment," in Proceedings of the 2018 International Conference on Electronics, Information, and Communication (ICEIC), pp.1-4, 2018.
  5. Y.F.Wang, Q.Huang and J.Hu. "Adaptive Enhancement for Low-Contrast Color Images via Histogram Modification and Saturation Adjustment," in Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC), PP.405-409, 2018.
  6. Z.Q.Ying, G.LI, Y.R.REN, R.G.Wang and W.M.Wang, "A New Image Contrast Enhancement Algorithm using Exposure Fusion Framework," in Proceedings of the 2017International Conference on Computer Analysis of Images and Patterns, pp.36-46, 2017.
  7. X.Dong, G.Wang, Y.Pang, W.X.Li and J.T.Wen, "Fast efficient algorithm for enhancement of low lighting video," in Proceedings of the 2011 IEEE International Conference on Multimedia and Expo, pp.1-6, 2011.
  8. D.J.Jonbson, Z.Rahman and G.A.Woodell, "A multiscale retinex for bridging the gap between color images and the human observation of scenes," IEEE Transactions on Image processing, Vol.6, No.7, pp.965-976, 1997. https://doi.org/10.1109/83.597272
  9. X.J.Guo, Y.LI and H.B.Ling, "LIME: low-light image enhancement via illumination map estimation," IEEE Trginsactions on Image Processing, Vol.26, No.2, pp.982-993, 2017. https://doi.org/10.1109/TIP.2016.2639450
  10. X.Y.Fu, D.L.Zeng, Y.Huang, Y.H.Liao, X.H.Ding and J.Paisey, "A fusion-based enhancing method for weakly illuminated images," Signal Processing, Vol.129, No.1, pp.82-96, 2016. https://doi.org/10.1016/j.sigpro.2016.05.031
  11. S.H.Wang, J.Zheng, H.M.Hu and B.Li, "Naturalness preserved enhancement algorithm for non-uniform illumination images," IEEE Transactions on Image Processing, Vol.22, No.9, pp.3538-3548, 2013.
  12. H.J.Liu, X.K.Sun, H.Han and W.Cao, "Low-light Video Image Enhancement Based on Multiscale Retinex-like Algorithm," in Proceedings of the 2016 Chinese Control and Decision Conference (CCDC), pp.3712-3715, 2016.
  13. D.X.Peng, T.Zhen and Z.H.Li, "A Survey of Research Methods for Low Light Image Enhancement," Computer Engineering and Applications, Vol.59, No.18, pp.14-27, 2023.
  14. F.Z.Song, Z.F.Wang, Z.X.Xie, "A method for measuring visual contrast resolution," Invention patent, China, ZL 200710078497.7, 2007.
  15. Y.Chen, Y.Li, X.F.Lv, Z.X.Xie and P.Feng, "Active assessment of color image quality based on visual perception," Optics and Precision Engineering, Vol.21, No.3, pp.742-750, 2013. https://doi.org/10.3788/OPE.20132103.0742
  16. L.Ma, T.Y.Ma and R.S.Liu, "The review of low-light image enhancement," Journal of Image and Graphica, Vol.27, No.5, pp.1392-1409, 2022.
  17. L.J.Wang, X.Chang and B.Y.Zhang, "Adaptive low-illumination image enhancement based on MSRCR," Modern Electronics Technique, Vol.45, No.2, pp.155-161, 2022.
  18. Q.J.Chen and Y.Gu, "A low-light image enhancement algorithm based on multi-scale depthwise separable convolution," Computer Engineering & Science, Vol.45, No.10, pp.1830-1837, 2023.