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The Visual Computer

, Volume 33, Issue 2, pp 151–161 | Cite as

Automatic estimation and segmentation of partial blur in natural images

  • Taiebeh Askari JavaranEmail author
  • Hamid Hassanpour
  • Vahid Abolghasemi
Original Article

Abstract

Digital images may contain undesired blurred regions. Automatic detection of such regions and estimation of the amount of blurriness in a given image are important issues in many computer vision applications. This paper presents a simple and effective method to automatically detect blurred regions. The proposed method consists of two main parts. First, a novel blur metric, which can significantly distinguish blur and non-blur regions, is proposed. This metric is then used to generate a blur map to encode the amount of blurriness for individual pixels in a given image. Finally, the estimated blur map is used to segment the input image into blurred/non-blurred regions by applying a pixon-based technique. The proposed approach is evaluated for out-of-focus and motion-blurred natural images. By conducting experiments on a large dataset containing real images with defocus blur and partial motion blur regions, qualitative and quantitative measures are performed. The obtained results in this paper show that the proposed approach outperforms the state-of-the-art methods for blur estimation in digital images.

Keywords

Partial blur Blur metric Blur map Blur/non-blur region segmentation Pixon 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Taiebeh Askari Javaran
    • 1
    Email author
  • Hamid Hassanpour
    • 1
  • Vahid Abolghasemi
    • 2
  1. 1.Image Processing and Data Mining (IPDM) Research Lab, Faculty of Computer Engineering and Information TechnologyUniversity of ShahroodShahroodIran
  2. 2.Faculty of Electrical Engineering and RoboticsUniversity of ShahroodShahroodIran

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