World From Blur

Jiayan Qiu, Xinchao Wang, Stephen J. Maybank, Dacheng Tao; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 8493-8504

Abstract


What can we tell from a single motion-blurred image? We show in this paper that a 3D scene can be revealed. Unlike prior methods that focus on producing a deblurred image, we propose to estimate and take advantage of the hidden message of a blurred image, the relative motion trajectory, to restore the 3D scene collapsed during the exposure process. To this end, we train a deep network that jointly predicts the motion trajectory, the deblurred image, and the depth one, all of which in turn form a collaborative and self-supervised cycle that supervise one another to reproduce the input blurred image, enabling plausible 3D scene reconstruction from a single blurred image. We test the proposed model on several large-scale datasets we constructed based on benchmarks, as well as real-world blurred images, and show that it yields very encouraging quantitative and qualitative results.

Related Material


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[bibtex]
@InProceedings{Qiu_2019_CVPR,
author = {Qiu, Jiayan and Wang, Xinchao and Maybank, Stephen J. and Tao, Dacheng},
title = {World From Blur},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}