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



Williams, Bryan M ORCID: 0000-0001-5930-287X, Spencer, Jack A, Chen, Ke ORCID: 0000-0002-6093-6623, Zheng, Yalin ORCID: 0000-0002-7873-0922 and Harding, Simon ORCID: 0000-0003-4676-1158
(2016) An effective variational model for simultaneous reconstruction and segmentation of blurred images. Journal of Algorithms & Computational Technology, 10 (4). 244 - 264.

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Abstract

<jats:p> 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. </jats:p>

Item Type: Article
Depositing User: Symplectic Admin
Date Deposited: 22 Sep 2016 16:12
Last Modified: 23 Jan 2021 17:12
DOI: 10.1177/1748301816660406
Open Access URL: http://act.sagepub.com/content/early/2016/08/01/17...
URI: https://livrepository.liverpool.ac.uk/id/eprint/3003433