Abstract
Object motion blur is a challenging problem as the foreground and the background in the scenes undergo different types of image degradation due to movements in various directions and speed. Most object motion deblurring methods address this problem by segmenting blurred images into regions where different kernels are estimated and applied for restoration. Segmentation on blurred images is difficult due to ambiguous pixels between regions, but it plays an important role for object motion deblurring. To address these problems, we propose a novel model for object motion deblurring. The proposed model is developed based on a maximum a posterior formulation in which soft-segmentation is incorporated for object layer estimation. We propose an efficient algorithm to jointly estimate object segmentation and camera motion where each layer can be deblurred well under the guidance of the soft-segmentation. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art object motion deblurring methods on challenging scenarios.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 |
| Publisher | IEEE Computer Society |
| Pages | 459-468 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781467388504 |
| DOIs | |
| Publication status | Published - 2016 Dec 9 |
| Event | 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States Duration: 2016 Jun 26 → 2016 Jul 1 |
Publication series
| Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
|---|---|
| Volume | 2016-December |
| ISSN (Print) | 1063-6919 |
Conference
| Conference | 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 |
|---|---|
| Country | United States |
| City | Las Vegas |
| Period | 16/6/26 → 16/7/1 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
Cite this
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Soft-segmentation guided object motion deblurring. / Pan, Jinshan; Hu, Zhe; Su, Zhixun; Lee, Hsin Ying; Yang, Ming Hsuan.
Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. IEEE Computer Society, 2016. p. 459-468 7780425 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2016-December).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Soft-segmentation guided object motion deblurring
AU - Pan, Jinshan
AU - Hu, Zhe
AU - Su, Zhixun
AU - Lee, Hsin Ying
AU - Yang, Ming Hsuan
PY - 2016/12/9
Y1 - 2016/12/9
N2 - Object motion blur is a challenging problem as the foreground and the background in the scenes undergo different types of image degradation due to movements in various directions and speed. Most object motion deblurring methods address this problem by segmenting blurred images into regions where different kernels are estimated and applied for restoration. Segmentation on blurred images is difficult due to ambiguous pixels between regions, but it plays an important role for object motion deblurring. To address these problems, we propose a novel model for object motion deblurring. The proposed model is developed based on a maximum a posterior formulation in which soft-segmentation is incorporated for object layer estimation. We propose an efficient algorithm to jointly estimate object segmentation and camera motion where each layer can be deblurred well under the guidance of the soft-segmentation. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art object motion deblurring methods on challenging scenarios.
AB - Object motion blur is a challenging problem as the foreground and the background in the scenes undergo different types of image degradation due to movements in various directions and speed. Most object motion deblurring methods address this problem by segmenting blurred images into regions where different kernels are estimated and applied for restoration. Segmentation on blurred images is difficult due to ambiguous pixels between regions, but it plays an important role for object motion deblurring. To address these problems, we propose a novel model for object motion deblurring. The proposed model is developed based on a maximum a posterior formulation in which soft-segmentation is incorporated for object layer estimation. We propose an efficient algorithm to jointly estimate object segmentation and camera motion where each layer can be deblurred well under the guidance of the soft-segmentation. Experimental results demonstrate that the proposed algorithm performs favorably against the state-of-the-art object motion deblurring methods on challenging scenarios.
UR - http://www.scopus.com/inward/record.url?scp=84986300554&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84986300554&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2016.56
DO - 10.1109/CVPR.2016.56
M3 - Conference contribution
AN - SCOPUS:84986300554
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 459
EP - 468
BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
PB - IEEE Computer Society
ER -