[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Multi-scale Contrastive Learning for Image Colorization

  • Conference paper
  • First Online:
Computer Vision and Machine Intelligence

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 586))

  • 716 Accesses

Abstract

This paper proposes a multi-scale contrastive learning technique for image colorization. The image colorization task aims to find a map between the source gray image and the predicted color image. Multi-scale contrastive learning for unpaired image colorization has been proposed to transform the gray patches in the input image into the color patches in the output image. The contrastive learning method uses input and output patches and maximizes their mutual information to infer an efficient mapping between the two domains. We propose a multi-scale approach for contrastive learning where the contrastive loss is determined from different resolutions of the source image to improve the color quality. We further illustrate the effectiveness of the proposed approach through experimental outcomes and comparisons with cutting-edge strategies in image colorization tasks.

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

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 159.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 199.99
Price includes VAT (United Kingdom)
  • 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

Institutional subscriptions

Similar content being viewed by others

References

  1. Joshi, M.R., Nkenyereye, L., Joshi, G.P., Islam, S.M., Abdullah-Al-Wadud, M., Shrestha, S.: Auto-colorization of historical images using deep convolutional neural networks. Mathematics 8(12), 2258 (2020)

    Article  Google Scholar 

  2. Sýkora, D., Buriánek, J., Žára, J.: Colorization of black-and-white cartoons. Image Vis. Comput. 23(9), 767–82 (2005)

    Article  Google Scholar 

  3. Toet, A.: Colorizing single band intensified night vision images. Displays 26(1), 15–21 (2005)

    Article  Google Scholar 

  4. Gravey, M., Rasera, L.G., Mariethoz, G.: Analogue-based colorization of remote sensing images using textural information. ISPRS J. Photogrammetry Remote Sens. 1(147), 242–54 (2019)

    Article  Google Scholar 

  5. Geshwind, D.M.: Method for colorizing black and white footage. US Patent 4,606,625 (1986)

    Google Scholar 

  6. Zhang, R., Isola, P., Efros, A.A., Colorful image colorization. In: European Conference on Computer Vision, vol. 8, pp. 649–666. Springer, Cham (2016)

    Google Scholar 

  7. Su, J.W., Chu, H.K., Huang, J.B.: Instance aware image colorization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7968–7977 (2020)

    Google Scholar 

  8. Park, T., Efros, A.A., Zhang, R., Zhu, J.Y.: Contrastive learning for unpaired image-to-image translation. In: European Conference on Computer Vision. pp. 319-345. Springer (2020)

    Google Scholar 

  9. Han, J., Shoeiby, M., Petersson, L., Armin, M.A.: Dual contrastive learning for unsupervised image-to-image translation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 746–755 (2021)

    Google Scholar 

  10. Oord, A.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)

  11. Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597-1607. PMLR (2020)

    Google Scholar 

  12. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: IEEE International Conference on Computer Vision (ICCV) (2017)

    Google Scholar 

  13. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with condi- tional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1125–1134 (2017)

    Google Scholar 

  14. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: NIPS (2014)

    Google Scholar 

  15. Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  16. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)

    Google Scholar 

  17. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools (2000)

    Google Scholar 

  18. Russakovsky, O.*, Deng, J.*, Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: (* = equal contribution) ImageNet large scale visual recognition challenge. IJCV (2015)

    Google Scholar 

  19. https://github.com/mseitzer/pytorch-fid

  20. Kumar, M., Weissenborn, D., Kalchbrenner, N.: Colorization transformer. In: ICLR (2021)

    Google Scholar 

  21. https://www.coursera.org/lecture/build-better-generative-adversarial-networks-gans/frechet-inception-distance-fid-LY8WK

  22. https://en.wikipedia.org/wiki/Frechet_distance

  23. https://www.oreilly.com/library/view/generative-adversarial-networks/9781789136678/9bf2e543-8251-409e-a811-77e55d0dc021.xhtml

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ketan Lambat .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lambat, K., Ghorai, M. (2023). Multi-scale Contrastive Learning for Image Colorization. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_27

Download citation

Publish with us

Policies and ethics