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PalGAN: Image Colorization with Palette Generative Adversarial Networks

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

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.

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References

  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!). https://github.com/jantic/DeOldify

  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). https://doi.org/10.1007/978-3-030-01258-8_27

    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). https://doi.org/10.1007/978-3-540-88690-7_10

    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). https://doi.org/10.1007/978-3-319-46493-0_35

    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). https://doi.org/10.1007/978-3-030-58595-2_45

    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). https://doi.org/10.1007/978-3-319-46487-9_40

    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 

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Acknowledgment

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

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Correspondence to Yu Qiao .

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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. https://doi.org/10.1007/978-3-031-19784-0_16

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  • DOI: https://doi.org/10.1007/978-3-031-19784-0_16

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