Abstract
Image deconvolution appears in many image-related problems. Previous works tried to train neural networks directly on blurry/clean pairs to restore clean images but failed. In this work, we propose a novel neural network, trained end-to-end, pixels-to-pixels, to deblur images from blurry ones. Our key insight is to build multi-scale convolutional neural networks that extract various scale feature maps which is essential for recovering sharp images and removing artifacts. The networks take input image of arbitrary size and produce output within efficient time. We demonstrate that our approach yields better result than the state-of-the-art deconvolution algorithms on a large dataset.
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References
Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2392–2399. IEEE (2012)
Cho, S., Lee, S.: Fast motion deblurring. ACM Trans. Graph. 28, 145 (2009)
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image restoration by sparse 3d transform-domain collaborative filtering. In: Electronic Imaging, p. 681207 (2008)
Danielyan, A., Katkovnik, V., Egiazarian, K.: BM3D frames and variational image deblurring. IEEE Trans. Image Process. 21(4), 1715–1728 (2012)
Eigen, D., Krishnan, D., Fergus, R.: Restoring an image taken through a window covered with dirt or rain. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 633–640 (2013)
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)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: AISTATS, vol. 9, pp. 249–256 (2010)
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)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, pp. 675–678. ACM (2014)
Krishnan, D., Fergus, R.: Fast image deconvolution using hyper-Laplacian priors. In: Advances in Neural Information Processing Systems, pp. 1033–1041 (2009)
Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM Trans. Graph. 26(3), 70 (2007)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the International Conference on Machine Learning, pp. 807–814 (2010)
Roth, S., Black, M.J.: Fields of experts: a framework for learning image priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 860–867. IEEE (2005)
Schmidt, U., Rother, C., Nowozin, S., Jancsary, J., Roth, S.: Discriminative non-blind deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 604–611 (2013)
Schuler, C.J., Christopher Burger, H., Harmeling, S., Scholkopf, B.: A machine learning approach for non-blind image deconvolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1067–1074 (2013)
Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)
Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Advances in Neural Information Processing Systems, pp. 341–349 (2012)
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
Xu, L., Ren, J.S., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. In: Advances in Neural Information Processing Systems, pp. 1790–1798 (2014)
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 479–486. IEEE (2011)
Acknowledgments
This work is supported by National Nature Science Foundation of China (61327013, 61379084, 61402440) and the Key Research Program of the Chinese Academy of Sciences, Grant No. KFZD-SW-407.
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Wang, X., Dai, F., Suo, J., Zhang, Y., Dai, Q. (2018). Multi-scale Convolutional Neural Networks for Non-blind Image Deconvolution. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_89
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