An effective variational model for simultaneous reconstruction and segmentation of blurred images

Williams, Bryan M. and Spencer, Jack A. and Chen, Ke and Zheng, Yalin and Harding, Simon (2016) An effective variational model for simultaneous reconstruction and segmentation of blurred images. Journal of Algorithms and Computational Technology, 10 (4). pp. 244-264. ISSN 1748-3026 (https://doi.org/10.1177/1748301816660406)

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Abstract

The segmentation of blurred images is of great importance. There have been several recent pieces of work to tackle this problem and to link the areas of image segmentation and image deconvolution in the case where the blur function κ is known or of known type, such as Gaussian, but not in the case where the blur function is not known due to a lack of robust blind deconvolution methods. Here we propose two variational models for simultaneous reconstruction and segmentation of blurred images with spatially invariant blur, without assuming a known blur or a known blur type. Based on our recent work in blind deconvolution, we present two solution methods for the segmentation of blurred images based on implicitly constrained image reconstruction and convex segmentation. The first method is aimed at obtaining a good quality segmentation while the other is aimed at improving the speed while retaining the quality. Our results demonstrate that, while existing models are capable of segmenting images corrupted by small amounts of blur, they begin to struggle when faced with heavy blur degradation or noise, due to the limitation of edge detectors or a lack of strict constraints. We demonstrate that our new algorithms are effective for segmenting blurred images without prior knowledge of the blur function, in the presence of noise and offer improved results for images corrupted by strong blur.

ORCID iDs

Williams, Bryan M., Spencer, Jack A., Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623, Zheng, Yalin and Harding, Simon;
  • Item type:Article
    ID code:88383
    Dates:
    Date
    Event
    31 December 2016
    Published
    29 November 2016
    Published Online
    15 March 2016
    Accepted
    30 October 2015
    Submitted
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Science > Mathematics
    Department:UNSPECIFIED
    Depositing user: Pure Administrator
    Date deposited:11 Mar 2024 09:33
    Last modified:11 Nov 2024 14:14
    URI:https://strathprints.strath.ac.uk/id/eprint/88383
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