Abstract

We present an effective blind image deblurring method based on a data-driven discriminative prior. Our work is motivated by the fact that a good image prior should favor clear images over blurred ones. In this work, we formulate the image prior as a binary classifier which can be achieved by a deep convolutional neural network (CNN). The learned prior is able to distinguish whether an input image is clear or not. Embedded into the maximum a posterior (MAP) framework, it helps blind deblurring in various scenarios, including natural, face, text, and low-illumination images. However, it is difficult to optimize the deblurring method with the learned image prior as it involves a non-linear CNN.Therefore, we develop an efficient numerical approach based on the half-quadratic splitting method and gradient descend algorithm to solve the proposed model. Furthermore, the proposed model can be easily extended to non-uniform deblurring. Both qualitative and quantitative experimental results show that our method performs favorably against state-of-the-art algorithms as well as domain-specific image deblurring approaches.

Technical Papers and Codes

[Conferrence Version] [Supplemental materials][Journal Version]

[Code ]

Bibtex

Conference version:

@InProceedings{li2018learning,

               title           = {Learning a Discriminative Prior for Blind Image Deblurring},

               author          = {Li, Lerenhan and Pan, Jinshan and Lai, Wei-Sheng and Gao, Changxin and Sang, Nong and                                Yang, Ming-Hsuan},

               booktitle       = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

               year            = {2018}

}

Journal version:

@article{li2019blind,

         title                = {Blind Image Deblurring via Deep Discriminative Priors},

         author               = {Li, Lerenhan and Pan, Jinshan and Lai, Wei-Sheng and Gao, Changxin and Sang, Nong and Yang, Ming-Hsuan},

         journal              = {International Journal of Computer Vision},

         year                 = {2019}

}

Experimental Results

Deblurred Results on the Köhler dataset

[Google Drive] : https://drive.google.com/open?id=1zcmsa3SJCuRzlfsvLWXB6fguMJR0Lx1D

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More results are included in Supplemental materials.

Natural Image Deblurring

Text Image Deblurring

Face Image Deblurring

Quantitative Evaluations on Natural Image Deblurring Datasets

References

[1] L. Xu, S. Zheng, and J. Jia. “Unnatural L₀ sparse representation for natural image deblurring”, CVPR 2013.

[2] J. Pan, Z. Hu, Z. Su, and M.-H. Yang. “Deblurring text images via L0-regularized intensity and gradient prior”, CVPR 2014.

[3] J. Pan, D. Sun, H. Pfister, and M.-H. Yang, "Blind Image Deblurring Using Dark Channel Prior", CVPR2016.

[4] Y. Yan, W. Ren, Y. Guo, R. Wang, and X. Cao, "Image Deblurring via Extreme Channels Prior ", CVPR2017.

[5] A. Levin, Y. Weiss, F. Durand, and W. T. Freeman, “Understanding and evaluating blind deconvolution algorithms,” CVPR 2009.

[6] R. Kohler, M. Hirsch, B. Mohler and B. Scholkopf. “Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database”, ECCV 2012.

[7] L. Sun, S. Cho, J.Wang, and J. Hays. "Edge-based blur kernel estimation using patch priors", ICCP 2013.