Skip to main content

PalGAN: Image Colorization with Palette Generative Adversarial Networks

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13675))

Included in the following conference series:


Multimodal ambiguity and color bleeding remain challenging in colorization. To tackle these problems, we propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention. To circumvent the multimodality issue, we present a new colorization formulation that estimates a probabilistic palette from the input gray image first, then conducts color assignment conditioned on the palette through a generative model. Further, we handle color bleeding with chromatic attention. It studies color affinities by considering both semantic and intensity correlation. In extensive experiments, PalGAN outperforms state-of-the-arts in quantitative evaluation and visual comparison, delivering notable diverse, contrastive, and edge-preserving appearances. With the palette design, our method enables color transfer between images even with irrelevant contexts.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
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

Institutional subscriptions

Similar content being viewed by others


  1. Afifi, M., Brubaker, M.A., Brown, M.S.: HistoGAN: controlling colors of GAN-generated and real images via color histograms. In: CVPR, pp. 7941–7950 (2021)

    Google Scholar 

  2. Antic, J.: A deep learning based project for colorizing and restoring old images (and video!).

  3. Bahng, H., et al.: Coloring with words: guiding image colorization through text-based palette generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 443–459. Springer, Cham (2018).

    Chapter  Google Scholar 

  4. Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)

  5. Caesar, H., Uijlings, J., Ferrari, V.: COCO-stuff: thing and stuff classes in context. In: CVPR, pp. 1209–1218 (2018)

    Google Scholar 

  6. Chang, H., Fried, O., Liu, Y., DiVerdi, S., Finkelstein, A.: Palette-based photo recoloring. TOG 34(4), 139 (2015)

    Article  Google Scholar 

  7. Charpiat, G., Hofmann, M., Schölkopf, B.: Automatic image colorization via multimodal predictions. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 126–139. Springer, Heidelberg (2008).

    Chapter  Google Scholar 

  8. Chia, A.Y.S., et al.: Semantic colorization with internet images. TOG 30(6), 1–8 (2011)

    Article  MathSciNet  Google Scholar 

  9. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248–255 (2009)

    Google Scholar 

  10. Deshpande, A., Lu, J., Yeh, M.C., Jin Chong, M., Forsyth, D.: Learning diverse image colorization. In: CVPR, pp. 6837–6845 (2017)

    Google Scholar 

  11. Goodfellow, I., et al.: Generative adversarial nets. In: NeurIPS, pp. 2672–2680 (2014)

    Google Scholar 

  12. Guadarrama, S., Dahl, R., Bieber, D., Norouzi, M., Shlens, J., Murphy, K.: Pixcolor: pixel recursive colorization. arXiv preprint arXiv:1705.07208 (2017)

  13. He, K., Sun, J., Tang, X.: Guided image filtering. TPAMI 35(6), 1397–1409 (2012)

    Article  Google Scholar 

  14. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)

    Google Scholar 

  15. He, M., Chen, D., Liao, J., Sander, P.V., Yuan, L.: Deep exemplar-based colorization. TOG 37(4), 1–16 (2018)

    Google Scholar 

  16. Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS, pp. 6626–6637 (2017)

    Google Scholar 

  17. Iizuka, S., Simo-Serra, E., Ishikawa, H.: Let there be color! joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. TOG 35(4), 1–11 (2016)

    Article  Google Scholar 

  18. Ironi, R., Cohen-Or, D., Lischinski, D.: Colorization by example. Render. Tech. 29, 201–210 (2005)

    Google Scholar 

  19. Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR, pp. 1125–1134 (2017)

    Google Scholar 

  20. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. arXiv preprint arXiv:1812.04948 (2018)

  21. Kim, E., Lee, S., Park, J., Choi, S., Seo, C., Choo, J.: Deep edge-aware interactive colorization against color-bleeding effects. In: ICCV, pp. 14667–14676 (2021)

    Google Scholar 

  22. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  23. Kumar, M., Weissenborn, D., Kalchbrenner, N.: Colorization transformer. arXiv preprint arXiv:2102.04432 (2021)

  24. Larsson, G., Maire, M., Shakhnarovich, G.: Learning representations for automatic colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 577–593. Springer, Cham (2016).

    Chapter  Google Scholar 

  25. Larsson, G., Maire, M., Shakhnarovich, G.: Colorization as a proxy task for visual understanding. In: CVPR (2017)

    Google Scholar 

  26. Lei, C., Chen, Q.: Fully automatic video colorization with self-regularization and diversity. In: CVPR, pp. 3753–3761 (2019)

    Google Scholar 

  27. Levin, A., Lischinski, D., Weiss, Y.: Colorization using optimization. In: SIGGRAPH, pp. 689–694 (2004)

    Google Scholar 

  28. Li, W., Lin, Z., Zhou, K., Qi, L., Wang, Y., Jia, J.: MAT: mask-aware transformer for large hole image inpainting. In: CVPR, pp. 10758–10768 (2022)

    Google Scholar 

  29. Liu, X., et al.: Intrinsic colorization. In: SIGGRAPH Asia, pp. 1–9 (2008)

    Google Scholar 

  30. Liu, X., Yin, G., Shao, J., Wang, X., et al.: Learning to predict layout-to-image conditional convolutions for semantic image synthesis. In: NeurIPS, pp. 570–580 (2019)

    Google Scholar 

  31. Liu, Z., Wang, Y., Qi, X., Fu, C.W.: Towards implicit text-guided 3d shape generation. In: CVPR, pp. 17896–17906, June 2022

    Google Scholar 

  32. Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957 (2018)

  33. Miyato, T., Koyama, M.: cGANs with projection discriminator. arXiv preprint arXiv:1802.05637 (2018)

  34. Nguyen, R.M., Price, B., Cohen, S., Brown, M.S.: Group-theme recoloring for multi-image color consistency. In: Computer Graphics Forum, vol. 36, pp. 83–92. Wiley Online Library (2017)

    Google Scholar 

  35. Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: CVPR, pp. 2337–2346 (2019)

    Google Scholar 

  36. Qi, L., et al.: Open-world entity segmentation. arXiv preprint arXiv:2107.14228 (2021)

  37. Qu, Y., Wong, T.T., Heng, P.A.: Manga colorization. TOG 25(3), 1214–1220 (2006)

    Article  Google Scholar 

  38. Su, J.W., Chu, H.K., Huang, J.B.: Instance-aware image colorization. In: CVPR, pp. 7968–7977 (2020)

    Google Scholar 

  39. Tai, Y.W., Jia, J., Tang, C.K.: Local color transfer via probabilistic segmentation by expectation-maximization. In: CVPR, vol. 1, pp. 747–754. IEEE (2005)

    Google Scholar 

  40. Torralba, A., Freeman, W.T.: Properties and applications of shape recipes (2002)

    Google Scholar 

  41. Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: CVPR, pp. 8798–8807 (2018)

    Google Scholar 

  42. Wang, Y., Chen, Y.-C., Tao, X., Jia, J.: VCNet: a robust approach to blind image inpainting. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 752–768. Springer, Cham (2020).

    Chapter  Google Scholar 

  43. Wang, Y., Chen, Y.C., Zhang, X., Sun, J., Jia, J.: Attentive normalization for conditional image generation. In: CVPR, pp. 5094–5103 (2020)

    Google Scholar 

  44. Wang, Y., Qi, L., Chen, Y.C., Zhang, X., Jia, J.: Image synthesis via semantic composition. In: ICCV, pp. 13749–13758 (2021)

    Google Scholar 

  45. Wang, Y., Tao, X., Qi, X., Shen, X., Jia, J.: Image inpainting via generative multi-column convolutional neural networks. In: NeurIPS (2018)

    Google Scholar 

  46. Wang, Y., Tao, X., Shen, X., Jia, J.: Wide-context semantic image extrapolation. In: CVPR (2019)

    Google Scholar 

  47. Welsh, T., Ashikhmin, M., Mueller, K.: Transferring color to greyscale images. In: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, pp. 277–280 (2002)

    Google Scholar 

  48. Wu, H., Zheng, S., Zhang, J., Huang, K.: Fast end-to-end trainable guided filter. In: CVPR, pp. 1838–1847 (2018)

    Google Scholar 

  49. Wu, Y., Wang, X., Li, Y., Zhang, H., Zhao, X., Shan, Y.: Towards vivid and diverse image colorization with generative color prior. In: ICCV, pp. 14377–14386 (2021)

    Google Scholar 

  50. Xia, M., Wang, Y., Han, C., Wong, T.T.: Enhance convolutional neural networks with noise incentive block. arXiv preprint arXiv:2012.12109 (2020)

  51. Xu, X., Wang, Y., Wang, L., Yu, B., Jia, J.: Conditional temporal variational autoencoder for action video prediction. arXiv preprint arXiv:2108.05658 (2021)

  52. Yatziv, L., Sapiro, G.: Fast image and video colorization using chrominance blending. TIP 15(5), 1120–1129 (2006)

    Google Scholar 

  53. Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. arXiv preprint arXiv:1805.08318 (2018)

  54. Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 649–666. Springer, Cham (2016).

    Chapter  Google Scholar 

  55. Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)

    Google Scholar 

  56. Zhang, R., et al.: Real-time user-guided image colorization with learned deep priors. arXiv preprint arXiv:1705.02999 (2017)

  57. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV, pp. 2223–2232 (2017)

    Google Scholar 

  58. Zomet, A., Peleg, S.: Multi-sensor super-resolution. In: Sixth IEEE Workshop on Applications of Computer Vision (WACV), pp. 27–31. IEEE (2002)

    Google Scholar 

Download references


This work is partially supported by the Shanghai Committee of Science and Technology (Grant No. 21DZ1100100).

Author information

Authors and Affiliations


Corresponding author

Correspondence to Yu Qiao .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 19044 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, Y., Xia, M., Qi, L., Shao, J., Qiao, Y. (2022). PalGAN: Image Colorization with Palette Generative Adversarial Networks. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13675. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19783-3

  • Online ISBN: 978-3-031-19784-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics