Motion blur analysis based on image segmentation and blind deconvolution

Chao Xing, Yanjun Li, Ke Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The problem of blurring caused by object motion in a gray level image is analyzed, and an algorithm combining image segmentation and blind deconvolution based on statistical features of objects and background is introduced to estimate visual motion and restore the image. Regions consisting certain geometrical information of pixels are regarded as suspected moving objects and segmented on the base of directional derivative of the image. Simple connected regions are selected by the use of mathematical morphological algorithm and level set method. Convolution kernels of regions larger than a given threshold are inferred through ensemble learning, and blurred regions can be restored individually. Radon transform is adopted to determine motion patterns of objects. Experimental results show the effectiveness of the algorithm for visual motion estimation and deblurring in a gray level image.

Original languageEnglish
Title of host publicationProceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
Pages1515-1519
Number of pages5
DOIs
StatePublished - 2010
Event2010 3rd International Congress on Image and Signal Processing, CISP 2010 - Yantai, China
Duration: 16 Oct 201018 Oct 2010

Publication series

NameProceedings - 2010 3rd International Congress on Image and Signal Processing, CISP 2010
Volume4

Conference

Conference2010 3rd International Congress on Image and Signal Processing, CISP 2010
Country/TerritoryChina
CityYantai
Period16/10/1018/10/10

Keywords

  • Level set method
  • Motion deblurring
  • Radon transform
  • Visual motion estimation

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