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.
|Title of host publication||Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016|
|Publisher||IEEE Computer Society|
|Number of pages||10|
|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
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016|
|Period||16/6/26 → 16/7/1|
Bibliographical noteFunding Information:
We thank T. H. Kim for providing the deblurred results of his methods [21, 22]. J. Pan is supported by a scholarship from China Scholarship Council. Z. Su is supported by the NSFC (No. 61572099 and 61320106008). Z. Hu and M.-H. Yang are supported in part by the NSF CAREER Grant (No. 1149783), NSF IIS Grant (No. 1152576).
© 2016 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition