Advertisement

2D Color Image Enhancement Based on Conditional Generative Adversarial Network and Interpolation

  • Yen-Ju Li
  • Chun-Hsiang Chang
  • Chitra Meghala Yelamandala
  • Yu-Cheng FanEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1036)

Abstract

In the rapid development of autonomous driving technology. Precise detection of objects might assist self-driving cars to drive as safely as human. The object detection is frequently uses point clouds and produces high quality environment color images to match. However, at night or when the light is dim, it affects the quality of color images. In order to overcome this, the existing image enhancement is focused on the histogram equalization method [1] and Retinex algorithm [2]. This paper proposes to use the Conditional Generative Adversarial Network (cGAN) [3] to train the intrinsic images for quickly decomposed shadow layer, and then use the interpolation method to achieve the image contrast enhancement.

References

  1. 1.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Upper Saddle River (2002)Google Scholar
  2. 2.
    Land, E.H., McCann, J.J.: Lightness and Retinex theory. J. Opt. Soc. Amer. 61(1), 1–11 (1971)CrossRefGoogle Scholar
  3. 3.
    Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR, pp. 2–5 (2017)Google Scholar
  4. 4.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)Google Scholar
  5. 5.
    Barrow, H.G., Tenenbaum, J.M.: Recovering intrinsic scene characteristics from images. In: Computer Vision Systems. Academic, New York (1978)Google Scholar
  6. 6.
    Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems IV, Academic Press (1994)Google Scholar
  7. 7.
    Grosse, R., Johnson, M.K., Adelson, E.H., Freeman, W.T.: Ground truth dataset and baseline evaluations or intrinsic image algorithms. In: International Conference on Computer Vision (2009)Google Scholar
  8. 8.
    Bell, S., Bala, K., Snavely, N.: Intrinsic images in the wild. ACM Trans. Graph. 33(4) (2014)CrossRefGoogle Scholar
  9. 9.
    Garces, E., Munoz, A., Lopez-Moreno, J., Gutierrez, D.: Intrinsic images by clustering. In: Computer Graphics Forum (Eurographics Symposium on Rendering), vol. 31, no. 4 (2012)CrossRefGoogle Scholar
  10. 10.
    Bi, S., Han, X., Yu, Y.: An L1 image transform for edgepreserving smoothing and scene-level intrinsic decomposition. ACM Trans. Graph. (TOG) 34(4), 78 (2015)CrossRefGoogle Scholar
  11. 11.
    Mittal, A., Soundararajan, R., Bovik, A.C.: Making a ‘Completely Blind’ image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)CrossRefGoogle Scholar
  12. 12.
    Wang, S., Zheng, J., Hu, H.M., Li, B.: Naturalness preserved emhancement algorithm for non-uniform illumination images. IEEE Trans. Image Process. 22(9), 3538–3578 (2013)CrossRefGoogle Scholar
  13. 13.
    Ye, Z., Mohamadian, H., Ye, Y.: Discrete entropy and relative entropy study on nonlinear clustering of underwater and arial image. In: Proceedings of IEEE International Conference on Control Applications, pp. 318–323, October 2007Google Scholar
  14. 14.
    Guo, X., Li, Y., Ling, H.: Lime: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2017)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Poynton, C.A., Kaufmann, M.: Digital Video and HDTV: Algorithm and Interfaces, pp. 260, 630 (2003)CrossRefGoogle Scholar
  16. 16.
    Ying, Z., Li, G., Ren, Y., Wang, R., Wang, W.: A new lowlight image enhancement algorithm using camera response model. In: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 3015–3022, October 2017Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yen-Ju Li
    • 1
  • Chun-Hsiang Chang
    • 1
  • Chitra Meghala Yelamandala
    • 1
  • Yu-Cheng Fan
    • 1
    Email author
  1. 1.Department of Electronic EngineeringNational Taipei University of TechnologyTaipeiTaiwan

Personalised recommendations