A New Method of Blind Deconvolution for Colour Fundus Retinal Images
Fundus retinal imaging is widely used in the diagnosis and management of eye disease. Blur commonly occurs in the acquisition and when it is severe the resulting loss of resolution hampers accurate clinical assessment. In this paper, we present a new technique to address this challenging problem. We make use of implicitly constrained image deblurring, which is known to provide improved results over unconstrained and explicitly constrained methods, and build this into a multi-channel variational framework for parametric deblurring. We propose a new method for automatically selecting the regularisation parameter in the absence of the true (sharp) image using vessel segmentation. We then modify the model to include a regularisation coefficient function which is dependent on an available image mask in order to avoid potential inaccuracies caused by the addition of artificial masks. We present experimental results to demonstrate the effectiveness of our new method.
Keywords: Semi-blind deconvolution, regularisation parameter selection, vessel segmentation, constrained deconvolution, fundus image
How to Cite:
Williams, B. M. & Chen, K. & Harding, S. P. & Zheng, Y., (2015) “A New Method of Blind Deconvolution for Colour Fundus Retinal Images”, Proceedings of the Ophthalmic Medical Image Analysis International Workshop 2(2015), 129-136. doi: https://doi.org/10.17077/omia.1036
Rights: Copyright © 2015 Bryan M. Williams, Ke Chen, Simon P. Harding, and Yalin Zheng