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Deep Blind Image Inpainting

  • Yang LiuEmail author
  • Jinshan Pan
  • Zhixun Su
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11935)

Abstract

Image inpainting is a challenging problem as it needs to fill the information of the corrupted regions. Most of the existing inpainting algorithms assume that the positions of the corrupted regions are known. Different from existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image from a corrupted input. Our algorithm is motivated by the residual learning algorithm which aims to learn the missing information in corrupted regions. However, directly using existing residual learning algorithms in image restoration does not well solve this problem as little information is available in the corrupted regions. To solve this problem, we introduce an encoder and decoder architecture to capture more useful information and develop a robust loss function to deal with outliers. Our algorithm can predict the missing information in the corrupted regions, thus facilitating the clear image restoration. Both qualitative and quantitative experimental demonstrate that our algorithm can deal with the corrupted regions of arbitrary shapes and performs favorably against state-of-the-art methods.

Keywords

Blind image inpainting Residual learning Encoder and decoder architecture CNN 

Notes

Acknowledgements

This work is supported in part by NSFC (Nos. 61572099, 61872421, and 61922043), NSF of Jiangsu Province (No. BK20180471).

References

  1. 1.
    Arbeláez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE TPAMI 33(5), 898–916 (2011)CrossRefGoogle Scholar
  2. 2.
    Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: Patchmatch: a randomized correspondence algorithm for structural image editing. ACM TOG 28(3), 24 (2009)CrossRefGoogle Scholar
  3. 3.
    Bertalmio, M., Bertozzi, A.L., Sapiro, G.: Navier-stokes, fluid dynamics, and image and video inpainting. In: CVPR, pp. 355–362 (2001)Google Scholar
  4. 4.
    Bertalmio, M., Vese, L., Sapiro, G., Osher, S.: Simultaneous structure and texture image inpainting. IEEE TIP 12, 882–889 (2003)Google Scholar
  5. 5.
    Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: SIGGRAPH, pp. 417–424 (2000)Google Scholar
  6. 6.
    Chen, Q., Koltun, V.: Photographic image synthesis with cascaded refinement networks. In: ICCV, pp. 1511–1520 (2017)Google Scholar
  7. 7.
    Dong, B., Ji, H., Li, J., Shen, Z., Xu, Y.: Wavelet frame based blind image inpainting. ACM TOG 32(2), 268–279 (2012)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE TPAMI 38(2), 295–307 (2016)CrossRefGoogle Scholar
  9. 9.
    Goodfellow, I.J., et al.: Generative Adversarial Networks. arXiv (2014)Google Scholar
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: ICCV, pp. 1026–1034 (2015)Google Scholar
  11. 11.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770–778 (2016)Google Scholar
  12. 12.
    Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: ICCV, pp. 5197–5206 (2015)Google Scholar
  13. 13.
    Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. 36(4), 1–14 (2017)CrossRefGoogle Scholar
  14. 14.
    Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. arXiv (2016)Google Scholar
  15. 15.
    Kim, J., Lee, J.K., Lee, K.M.: Accurate image super-resolution using very deep convolutional networks. In: CVPR, pp. 1646–1654 (2016)Google Scholar
  16. 16.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv (2014) Google Scholar
  17. 17.
    Levin, A., Zomet, A., Weiss, Y.: Learning how to inpaint from global image statistics. In: ICCV, vol 1, pp. 305–312 (2003)Google Scholar
  18. 18.
    Li, Y., Liu, S., Yang, J., Yang, M.H.: Generative face completion. In: CVPR, pp. 3911–3919 (2017)Google Scholar
  19. 19.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV, pp. 416–423 (2001)Google Scholar
  20. 20.
    Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders - feature learning by inpainting. In: CVPR, pp. 2536–2544 (2016)Google Scholar
  21. 21.
    Ren, J.S.J., Xu, L., Yan, Q., Sun, W.: Shepard convolutional neural networks. In: NIPS, pp. 901–909 (2015)Google Scholar
  22. 22.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. arXiv (2015)Google Scholar
  23. 23.
    Roth, S., Black, M.J.: Fields of experts. IJCV 82(2), 205–229 (2009)CrossRefGoogle Scholar
  24. 24.
    Shi, W., et al.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: CVPR, pp. 1874–1883 (2016)Google Scholar
  25. 25.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv (2014)Google Scholar
  26. 26.
    Sun, J., Cao, W., Xu, Z., Ponce, J.: Learning a convolutional neural network for non-uniform motion blur removal. arXiv (2015)Google Scholar
  27. 27.
    Telea, A.: An image inpainting technique based on the fast marching method. J. Graph. Tools 9(1), 23–34 (2004)CrossRefGoogle Scholar
  28. 28.
    Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: NIPS, pp. 341–349 (2012)Google Scholar
  29. 29.
    Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., Li, H.: High-resolution image inpainting using multi-scale neural patch synthesis. arXiv (2016)Google Scholar
  30. 30.
    Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. arXiv (2017)Google Scholar
  31. 31.
    Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: ICCV, pp. 479–486 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Mathematical ScienceDalian University of TechnologyDalianChina
  2. 2.School of Computer ScienceNanjing University of Science and TechnologyNanjingChina

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