Fast Blur Detection and Parametric Deconvolution of Retinal Fundus Images
Blur is a significant problem in medical imaging which can hinder diagnosis and prevent further automated or manual processing. The problem of restoring an image from blur degradation remains a challenging task in image processing. Semi-blind deblurring is a useful technique which may be developed to restore the underlying sharp image given some assumed or known information about the cause of degradation. Existing models assume that the blur is of a particular type, such as Gaussian, and do not allow for the approximation of images corrupted by other blur types which are not easily incorporated into deblurring frameworks. We present an automated approach to image deconvolution which assumes that the cause of blur belongs to a set of common types. We develop a hierarchical approach with convolutional neural networks (CNNs) to distinguish between blur types, achieving an accuracy of 0.96 across a test set of 900 images, and to determine the blur strength, achieving accuracy of 0.77 across 1500 test images. Given this, we are able to reconstruct the underlying image to mean ISNR of 7.53.
KeywordsDeconvolution Convolutional neural networks Colour fundus Retina Parametric
This project is funded by the National Institute for Health Research’s i4i Programme. This paper summarises independent research funded by the National Institute for Health Research (NIHR) under its i4i Programme (Grant Reference Number II-LA-0813-20005). B. Al-Bander acknowledges financial support from the Higher Committee for Education Development in Iraq (Grant No. 182).
- 4.Abdel-Hamid, O., Mohamed, A.-R., Jiang, H., Deng, L., Penn, G., Yu, D.: Convolutional neural networks for speech recognition. IEEE/ACM TASLP 22(10), 1533–1545 (2014)Google Scholar
- 6.Schuler, C.J., Christopher Burger, H., Harmeling, S., Scholkopf, B.: A machine learning approach for non-blind image deconvolution. In: CVPR, 2013, pp. 1067–1074Google Scholar
- 8.Xu, L., Ren, J.S., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. In: NIPS, pp. 1790–1798 (2014)Google Scholar
- 9.Levin, A.: Blind motion deblurring using image statistics. In: Advances in Neural Information Processing Systems (NIPS) (2007)Google Scholar
- 10.Sun, J., Cao, W., Xu, Z., Ponce, J.: Learning a convolutional neural network for non-uniform motion blur removal. In: CVPR, pp. 769–777 (2015)Google Scholar
- 11.LeCun, Y., Jackel, L., Bottou, L., Brunot, A., Cortes, C., Denker, J., Drucker, H., Guyon, I., Muller, U., Sackinger, E., et al.: Comparison of learning algorithms for handwritten digit recognition. In: ICANN, vol. 60, pp. 53–60 (1995)Google Scholar