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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2021: Intelligent Data Engineering and Automated Learning – IDEAL 2021 pp 234–245Cite as

DC-Deblur: A Dilated Convolutional Network for Single Image Deblurring

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13113))

Abstract

Single image deblurring is a significant and challenging task in image processing vision and machine learning. Convolutional Neural Network (CNN) based models for deblurring often have a complex structure and a considerable number of parameters compared with those for other image restoration tasks such as image denoising, dehazing and super-resolution. The main reason is the requirement of large reception fields, which are important to image deblurring due to possible large blur kernels. Dilated convolution is a useful way to increase reception field without adding extra parameters. In this paper, we propose a novel network by adopting a dilated convolution structure, and we further improve the training process by combining L1 loss, MS-SSIM loss and MSE loss. The proposed network is light and fast. Quantitative and qualitative experiments indicate that our method outperforms state-of-the-art models, in terms of performance and speed.

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References

  1. Chen, D., et al.: Gated context aggregation network for image dehazing and deraining. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1375–1383. IEEE (2019)

    Google Scholar 

  2. Dai, J., Li, Y., He, K., Sun, J.: R-FCN: object detection via region-based fully convolutional networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 379–387 (2016)

    Google Scholar 

  3. Gao, H., Tao, X., Shen, X., Jia, J.: Dynamic scene deblurring with parameter selective sharing and nested skip connections. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3848–3856 (2019)

    Google Scholar 

  4. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)

    Google Scholar 

  5. Holschneider, M., Kronland-Martinet, R., Morlet, J., Tchamitchian, P.: A real-time algorithm for signal analysis with the help of the wavelet transform. In: Combes, J.M., Grossmann, A., Tchamitchian, P. (eds.) Wavelets, pp. 286–297. Springer, Heidelberg (1990). https://doi.org/10.1007/978-3-642-75988-8_28

  6. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708 (2017)

    Google Scholar 

  7. Kalchbrenner, N., et al.: Video pixel networks. In: International Conference on Machine Learning, pp. 1771–1779 (2017)

    Google Scholar 

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

  9. Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: Deblurgan: blind motion deblurring using conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8183–8192 (2018)

    Google Scholar 

  10. Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 8878–8887 (2019)

    Google Scholar 

  11. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 2169–2178 (2006)

    Google Scholar 

  12. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)

    Article  Google Scholar 

  13. Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 136–144 (2017)

    Google Scholar 

  14. Liu, X., Suganuma, M., Sun, Z., Okatani, T.: Dual residual networks leveraging the potential of paired operations for image restoration. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7007–7016 (2019)

    Google Scholar 

  15. Nah, S., Hyun Kim, T., Mu Lee, K.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3883–3891 (2017)

    Google Scholar 

  16. Oord, A.v.d., et al.: Wavenet: a generative model for raw audio. arXiv preprint arXiv:1609.03499 (2016)

  17. Pan, J., et al.: Learning dual convolutional neural networks for low-level vision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3070–3079 (2018)

    Google Scholar 

  18. Shen, Z., et al.: Human-aware motion deblurring. In: Proceedings of the IEEE Conference on Computer Vision (ICCV), pp. 5572–5581 (2019)

    Google Scholar 

  19. Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8174–8182 (2018)

    Google Scholar 

  20. Wang, Z., Ji, S.: Smoothed dilated convolutions for improved dense prediction. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2486–2495 (2018)

    Google Scholar 

  21. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)

  22. Zeng, Y., van der Lubbe, J.C., Loog, M.: Multi-scale convolutional neural network for pixel-wise reconstruction of van gogh’s drawings. Mach. Vis. Appl. 30(7–8), 1229–1241 (2019)

    Article  Google Scholar 

  23. Zhang, J., et al.: Dynamic scene deblurring using spatially variant recurrent neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2521–2529 (2018)

    Google Scholar 

  24. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)

    Article  MathSciNet  Google Scholar 

  25. Zhang, S., He, F., Ren, W.: Photo-realistic dehazing via contextual generative adversarial networks. Mach. Vis. Appl. 31(5), 33 (2020)

    Article  Google Scholar 

  26. Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging 3(1), 47–57 (2016)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. The University of Manchester, Manchester, M13 9PL, UK

    Boyan Xu & Hujun Yin

Authors
  1. Boyan Xu
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  2. Hujun Yin
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Corresponding author

Correspondence to Hujun Yin .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Universidad Politecnica de Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

  4. University of Manchester, Manchester, UK

    Richard Allmendinger

  5. University of Huelva, Huelva, Spain

    Antonio J. Tallón-Ballesteros

  6. Southern University of Science and Technology, Shenzhen, China

    Ke Tang

  7. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  8. University of Minho, Braga, Portugal

    Paulo Novais

  9. NOVA University of Lisbon, Lisbon, Portugal

    Susana Nascimento

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Xu, B., Yin, H. (2021). DC-Deblur: A Dilated Convolutional Network for Single Image Deblurring. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2021. IDEAL 2021. Lecture Notes in Computer Science(), vol 13113. Springer, Cham. https://doi.org/10.1007/978-3-030-91608-4_24

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  • DOI: https://doi.org/10.1007/978-3-030-91608-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91607-7

  • Online ISBN: 978-3-030-91608-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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