Soft-segmentation guided object motion deblurring

Jinshan Pan, Zhe Hu, Zhixun Su, Hsin Ying Lee, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
PublisherIEEE Computer Society
Pages459-468
Number of pages10
ISBN (Electronic)9781467388504
DOIs
Publication statusPublished - 2016 Dec 9
Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States
Duration: 2016 Jun 262016 Jul 1

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Volume2016-December
ISSN (Print)1063-6919

Conference

Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
CountryUnited States
CityLas Vegas
Period16/6/2616/7/1

Bibliographical note

Funding 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).

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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