Williams BM, Spencer JA, Chen K, Zheng Y, Harding S. An effective variational model for simultaneous reconstruction and segmentation of blurred images. Journal of Algorithms & Computational Technology. 2016 Dec;10(4):244-64.
https://journals.sagepub.com/doi/pdf/10.1177/1748301816660406
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 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.
