Skip to main content
SpringerLink
Log in
Book cover

International Conference on Network-Based Information Systems

NBiS - 2019 2019: Advances in Networked-based Information Systems pp 86–95Cite as

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

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Upper Saddle River (2002)

    Google Scholar 

  2. Land, E.H., McCann, J.J.: Lightness and Retinex theory. J. Opt. Soc. Amer. 61(1), 1–11 (1971)

    Article  Google Scholar 

  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. Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)

    Google Scholar 

  5. Barrow, H.G., Tenenbaum, J.M.: Recovering intrinsic scene characteristics from images. In: Computer Vision Systems. Academic, New York (1978)

    Google Scholar 

  6. Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems IV, Academic Press (1994)

    Google Scholar 

  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. Bell, S., Bala, K., Snavely, N.: Intrinsic images in the wild. ACM Trans. Graph. 33(4) (2014)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  11. Mittal, A., Soundararajan, R., Bovik, A.C.: Making a ‘Completely Blind’ image quality analyzer. IEEE Signal Process. Lett. 20(3), 209–212 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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 2007

    Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  15. Poynton, C.A., Kaufmann, M.: Digital Video and HDTV: Algorithm and Interfaces, pp. 260, 630 (2003)

    Chapter  Google Scholar 

  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 2017

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan

    Yen-Ju Li, Chun-Hsiang Chang, Chitra Meghala Yelamandala & Yu-Cheng Fan

Authors
  1. Yen-Ju Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chun-Hsiang Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chitra Meghala Yelamandala
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu-Cheng Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yu-Cheng Fan .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Division of Computer Science and Intelligent Systems, Faculty of Science and Technology, Oita University, Oita, Japan

    Hiroaki Nishino

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

  4. Department of Advanced Sciences, Faculty of Science and Engineering, Hosei University, Tokyo, Japan

    Makoto Takizawa

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, YJ., Chang, CH., Yelamandala, C.M., Fan, YC. (2020). 2D Color Image Enhancement Based on Conditional Generative Adversarial Network and Interpolation. In: Barolli, L., Nishino, H., Enokido, T., Takizawa, M. (eds) Advances in Networked-based Information Systems. NBiS - 2019 2019. Advances in Intelligent Systems and Computing, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-030-29029-0_8

Download citation

Publish with us

Policies and ethics

Search

Navigation