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From Motion Blur to Motion Flow: a Deep Learning Solution for
Removing Heterogeneous Motion Blur

Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton van den Hengel, Qinfeng Shi

framework 

Abstract

Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but the extensive literature on the subject indicates the difficulty in identifying a prior which is suitably informative, and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data. Learning a prior over the latent image would require modeling all possible image content. The critical observation underpinning our approach is thus that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content. This is a much easier learning task, but it also avoids the iterative process through which latent image priors are typically applied. Our approach directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow. Our FCN is the first universal end-to-end mapping from the blurred image to the dense motion flow. To train the FCN, we simulate motion flows to generate synthetic blurred-image-motion-flow pairs thus avoiding the need for human labeling. Extensive experiments on challenging realistic blurred images demonstrate that the proposed method outperforms the state-of-the-art.

Model

framework 
framework 

Papers

Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton van den Hengel, Qinfeng Shi. From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[Paper] [Supp]

* More results are in the supplementary material.

Code

Bibtex

@InProceedings{gong2017blur2mf,
author = {Gong, Dong and Yang, Jie and Liu, Lingqiao and Zhang, Yanning and Reid, Ian and Shen, Chunhua and van den Hengel, Anton and Shi, Qinfeng},
title = {From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}

Other Deblurring Work

  • Dong Gong, Mingkui Tan, Yanning Zhang, Anton van den Hengel, Qinfeng Shi. Self-paced Kernel Estimation for Robust Blind Image Deblurring. In IEEE International Conference on Computer Vision (ICCV), 2017. [Paper] [Data&Results]

  • Dong Gong, Mingkui Tan, Yanning Zhang, Anton van den Hengel, Qinfeng Shi. Blind Image Deconvolution by Automatic Gradient Activation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. [Paper] [Supp] [Code coming soon]