Abstract
Deblurring aims to restore clear images from blurred ones. Recently deep learning are widely used. Previous methods regard deblurring as dense prediction problems and rarely consider the inverse operation of blur. In this paper, we propose a multi-scale deformable deblurring kernel prediction network for dynamic scene deblurring which uses a coarse-to-fine method to predict the per-pixel deformable deblurring kernel and uses the fusion weight to integrate the latent images in different scales. Since the spatially variable blur scatters pixel information to surrounding sub-pixels and leads to the spatially and quantitively uneven distribution of latent pixel information, the per-pixel deformable deblurring kernel can adaptively select the sub-pixels and linearly combine them into the clean pixel for information aggregation. The multi-scale architecture helps the deformable deblurring kernel enlarge the reception field. The residual image is added to convolution result in each scale to supply refined edges when the kernel cannot cover the areas existing latent pixel information. Besides, we add local similarity loss to constrain deformable deblurring kernel’s weight and offset which boosts the deblurring performance. Qualitative and quantitative experiments show that our method can produce competitive deblurring performance.
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
Bertasius, G., Torresani, L., Shi, J.: Object detection in video with spatiotemporal sampling networks. In: Proceedings of the European Conference on Computer Vision, pp. 331–346 (2018)
Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764–773 (2017)
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, pp. 3848–3856 (2019)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Jo, Y., Oh, S.W., Kang, J., Kim, S.J.: Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3224–3232 (2018)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Krishnan, D., Tay, T., Fergus, R.: Blind deconvolution using a normalized sparsity measure. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 233–240 (2011)
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, pp. 8183–8192 (2018)
Liu, S., Pan, J., Yang, M.H.: Learning recursive filters for low-level vision via a hybrid neural network. In: Proceedings of the European Conference on Computer Vision, pp. 560–576 (2016)
Nah, S., et al.: Ntire 2019 challenge on video deblurring and super-resolution: dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)
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, pp. 3883–3891 (2017)
Niklaus, S., Mai, L., Liu, F.: Video frame interpolation via adaptive convolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 670–679 (2017)
Pan, J., Hu, Z., Su, Z., Yang, M.H.: Deblurring text images via l0-regularized intensity and gradient prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2901–2908 (2014)
Pan, J., Sun, D., Pfister, H., Yang, M.H.: Blind image deblurring using dark channel prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1628–1636 (2016)
Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional lstm network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)
Sim, H., Kim, M.: A deep motion deblurring network based on per-pixel adaptive kernels with residual down-up and up-down modules. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)
Su, S., Delbracio, M., Wang, J., Sapiro, G., Heidrich, W., Wang, O.: Deep video deblurring for hand-held cameras. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1279–1288 (2017)
Sun, X., Xiao, B., Wei, F., Liang, S., Wei, Y.: Integral human pose regression. In: Proceedings of the European Conference on Computer Vision, pp. 529–545 (2018)
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, pp. 8174–8182 (2018)
Tian, Y., Zhang, Y., Fu, Y., Xu, C.: Tdan: temporally-deformable alignment network for video super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3360–3369 (2020)
Wang, X., Chan, K.C., Yu, K., Dong, C., Change Loy, C.: Edvr: video restoration with enhanced deformable convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)
Xu, Y.S., Tseng, S.Y.R., Tseng, Y., Kuo, H.K., Tsai, Y.M.: Unified dynamic convolutional network for super-resolution with variational degradations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 12496–12505 (2020)
Yan, Y., Ren, W., Guo, Y., Wang, R., Cao, X.: Image deblurring via extreme channels prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4003–4011 (2017)
Yu, J., Fan, Y., Yang, J., Xu, N., Wang, Z., Wang, X., Huang, T.: Wide activation for efficient and accurate image super-resolution. arXiv preprint arXiv:1808.08718 (2018)
Zhang, H., Dai, Y., Li, H., Koniusz, P.: Deep stacked hierarchical multi-patch network for image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5978–5986 (2019)
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, pp. 2521–2529 (2018)
Zhou, S., Zhang, J., Pan, J., Xie, H., Zuo, W., Ren, J.: Spatio-temporal filter adaptive network for video deblurring. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2482–2491 (2019)
Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets v2: more deformable, better results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9308–9316 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, K., Sang, N. (2021). Multi-scale Deformable Deblurring Kernel Prediction for Dynamic Scene Deblurring. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12890. Springer, Cham. https://doi.org/10.1007/978-3-030-87361-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-87361-5_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87360-8
Online ISBN: 978-3-030-87361-5
eBook Packages: Computer ScienceComputer Science (R0)