Abstract
We propose a new and effective image deblurring network based on deep learning. The motivation of this work is based on traditional algorithms and deep learning which take an easy-to-difficult approach to image deblurring. In traditional algorithms, a rough blur kernel is obtained first, and then a precise blur kernel is gradually refined. In deep learning, the pyramid structure is adopted to restore clear images from easy to difficult. We hope to recover the clear image by two-way approximation. One network recovers the roughly clear image from the blurred image, and the other network recovers part of the structural information from the blank image, and finally the two networks are added together to obtain the clear image. Experiments show that since we decomposed the original deblurring task into two different tasks, the network performance has been effectively improved. Compared with other latest networks, our network can get clearer images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Trans. Graph. 27, 1–10 (2008)
Cho, S., Lee, S.: Fast Motion Deblurring. ACM Trans. Graph. 28, 1–8 (2009)
Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. ACM Trans. Graph. 25, 787–794 (2006)
Xu, L., Jia, J.: Two-phase kernel estimation for robust motion deblurring. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 157–170. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15549-9_12
Köhler, R., Hirsch, M., Mohler, B., Schölkopf, B., Harmeling, S.: Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7578, pp. 27–40. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33786-4_3
Xu, L., Zheng, S., Jia, J.: Unnatural l0 sparse representation for natural image deblurring. In: CVPR (2013)
J. Pan, Z. Hu, Z. Su, Yang, M.H.: L0-regularized intensity and gradient prior for deblurring text images and beyond. IEEE Trans. Pattern Anal. Mach. Intell. 39(2), 342C355 (2017)
Pan, J., Sun, D., Pfister, H., Yang, M.H.: Blind image deblurring using dark channel prior. In: CVPR (2016)
Pan, J., Hu, Z., Su, Z., Yang, M.-H.: Deblurring face images with exemplars. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 47–62. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10584-0_4
Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: CVPR (2017)
Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: CVPR (2018)
Gao, H., Tao, X., Shen, X., Jia, J.: Dynamic scene deblurring with parameter selective sharing and nested skip connections. In: CVPR (2019)
Zhang, H., Dai, Y., Li, H., Koniusz, P.: Deep stacked hierarchical multi-patch network for image deblurring. In: CVPR (2019)
Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: DeblurGAN: blind motion deblurring using conditional adversarial networks. In: CVPR (2018)
Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: DeblurGAN-v2: deblurring (Ordersof-Magnitude) faster and better. In: ICCV (2019)
Aljadaany, R., Pal, D.K., Savvides, M.: Douglas-Rachford networks: learning both the image prior and data fidelity terms for blind image deconvolution. In: CVPR (2019)
Zhang, K., Luo, W., Zhong, Y., et al.: Deblurring by realistic blurring. arXiv (2020)
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software available from tensorflow.org
Chakrabarti, A.: A neural approach to blind motion deblurring. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 221–235. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_14
Schuler, C.J., Hirsch, M., Harmeling, S., Scholkopf, B.: Learning to deblur. TPAMI 38(7), 1439–1451 (2016)
Sun, J., Cao, W., Xu, Z., Ponce, J.: Learning a convolutional neural network for non-uniform motion blur removal. In: CVPR (2015)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS, pp. 249–256 (2010)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600612 (2004)
Bahat, Y., Efrat, N., Irani, M.: Non-uniform Blind Deblurring by Reblurring. In: ICCV (2017)
Cai, J., Zuo, W., Zhang, L.: Dark and bright channel prior embedded network for dynamic scene deblurring. IEEE Trans. Image Process. 29, 6885–6897 (2020)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2014)
Acknowledgement
This work was supported in part by National Natural Science Foundation of China (No. 61801398), The Young Scholars Reserve Talents program of Xihua University and The program for Vehicle Measurement, Control and Safety Key Laboratory of Sichuan Province (No. QCCK2019-005) and The Innovation and Entrepreneurship Project of Xihua Cup (No. 2021055) and The Talent plan of Xihua College of Xihua University (No. 020200107).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Cheng, Z., Luo, B., Xu, L., Li, S., Xiao, K., Pei, Z. (2021). Multi-scale Two-Way Deblurring Network for Non-uniform Single Image Deblurring. In: Xiong, J., Wu, S., Peng, C., Tian, Y. (eds) Mobile Multimedia Communications. MobiMedia 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 394. Springer, Cham. https://doi.org/10.1007/978-3-030-89814-4_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-89814-4_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89813-7
Online ISBN: 978-3-030-89814-4
eBook Packages: Computer ScienceComputer Science (R0)