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World From Blur
- Source :
- CVPR
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
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.
- Subjects :
- business.industry
Computer science
Deep learning
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Process (computing)
02 engineering and technology
010501 environmental sciences
01 natural sciences
Motion (physics)
Image (mathematics)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Computer vision
Artificial intelligence
Focus (optics)
business
ComputingMethodologies_COMPUTERGRAPHICS
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Accession number :
- edsair.doi...........03839e59fb5a1f2427fb45ffc555b91b