Skip to main content
SpringerLink
Log in
Book cover

Smart Computer Vision pp 253–271Cite as

Convolution Neural Network and Auto-encoder Hybrid Scheme for Automatic Colorization of Grayscale Images

  • Chapter
  • First Online:

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

Conversion of grayscaled images to color images without human intervention is the subject of various researches within communities of machine learning (ML) and artificial intelligence (AI). The field of computer vision paved the way for improvement from video restoration to improved interpretability. The chapter has taken care for the problem of hallucinating an appreciable color version of a picture. Most researcher used statistical techniques that have its own limitations in automation. The proposed method helps in producing vibrant and realistic colors by hybridizing convolution neural network with auto-encoder. The learning process is addressed by classification over various iterative process to augment the variability of colors. The proposed work aims in converting a grayscale picture into a color picture and obtained classification accuracy of 66.99% with Adam optimizer and helps in automation without human intervention with better learning process.

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   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
Price excludes VAT (USA)
  • Durable hardcover 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. Varga, D., & Szirányi, T. (2016). Fully automatic image colorization based on Convolutional Neural Network. In 2016 23rd International Conference on Pattern Recognition (ICPR) (pp. 3691–3696). IEEE.

    Google Scholar 

  2. Salve, S., Shah, T., Ranjane, V., & Sadhukhan, S. (2018). Notice of violation of IEEE publication principles: Automatization of coloring grayscale images using convolutional neural network. In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 1171–1175). IEEE.

    Google Scholar 

  3. Putri, V. K., & Fanany, M. I. (2017). Sketch plus colorization deep convolutional neural networks for photos generation from sketches. In 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (pp. 1–6). IEEE.

    Google Scholar 

  4. Levin, A., Lischinski, D., & Weiss, Y. (2004). Colorization using optimization. In ACM SIGGRAPH 2004 Papers (pp. 689–694).

    Google Scholar 

  5. Huang, Y. C., Tung, Y. S., Chen, J. C., Wang, S. W., & Wu, J. L. (2005). An adaptive edge detection based colorization algorithm and its applications. Proceedings of the 13th annual ACM international conference on Multimedia, 351–354.

    Google Scholar 

  6. Yatziv, L., & Sapiro, G. (2006). Fast image and video colorization using chrominance blending. IEEE Transactions on Image Processing, 15(5), 1120–1129.

    Article  Google Scholar 

  7. Reinhard, E., Ashikhmin, M., Gooch, B., & Shirley, P. (2001). Color transfer between images. IEEE Computer Graphics and Applications, 21(5), 34–41.

    Article  Google Scholar 

  8. Welsh, T., Ashikhmin, M., & Mueller, K. (2002). Transferring color to greyscale images. ACM Transactions on Graphics, 21(3), 277–280.

    Article  Google Scholar 

  9. R. Irony, D. Cohen-Or, and D. Lischinski. Colorization by example. Eurographics Symp. on Rendering, 2005.

    Google Scholar 

  10. Charpiat, G., Hofmann, M., & Sch¨olkopf, B. (2008). Automatic image colorization via multi-modal predictions. Computer Vision-ECCV, 126, 2008–139.

    Google Scholar 

  11. Bugeau, A., & Ta, V. T. (2012). Patch-based image colorization. Proceedings of the IEEE International Conference on Pattern Recognition, 3058–3061.

    Google Scholar 

  12. Cheng, Z., Yang, Q., & Sheng, B. (2015). Deep colorization. Proceedings of the IEEE International Conference on Computer Vision, 415–423.

    Google Scholar 

  13. Dahl, R. (2016, January). Automatic colorization. http://tinyclouds.org/colorize/

Download references

Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    A. Anitha

  2. B-2-18, Block B, Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya (UM), Kuala Lumpur, Malaysia

    P. Shivakumara

  3. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Shreyansh Jain & Vidhi Agarwal

Authors
  1. A. Anitha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Shivakumara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shreyansh Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vidhi Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Anitha .

Editor information

Editors and Affiliations

  1. PSG College of Technology, Coimbatore, Tamil Nadu, India

    B. Vinoth Kumar

  2. Thiruvettakudy, National Institute of Technology, Puducherry, Karaikal, India

    P. Sivakumar

  3. Thiruvettakudy, National Institute of Technology, Puducherry, Karaikal, India

    B. Surendiran

  4. University of North Texas, Denton, TX, USA

    Junhua Ding

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Anitha, A., Shivakumara, P., Jain, S., Agarwal, V. (2023). Convolution Neural Network and Auto-encoder Hybrid Scheme for Automatic Colorization of Grayscale Images. In: Kumar, B.V., Sivakumar, P., Surendiran, B., Ding, J. (eds) Smart Computer Vision. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-20541-5_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20541-5_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20540-8

  • Online ISBN: 978-3-031-20541-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation