Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chan, T.F., Wong, C.-K.: Total variation blind deconvolution. IEEE T. Image Process. 7(3), 370–375 (1998)
Williams, B.M., Chen, K., Harding, S.P.: A new constrained total variational deblurring model and its fast algorithm. Numer. Algorithms 69(2), 415–441 (2015)
Almeida, M.S.C., Almeida, L.B.: Blind and semi-blind deblurring of natural images. IEEE T. Image Process. 19(1), 36–52 (2010)
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)
Zeng, T., Li, R., Mukkamala, R., Ye, J., Ji, S.: Deep convolutional neural networks for annotating gene expression patterns in the mouse brain. BMC Bioinform. 16(1), 147 (2015)
Schuler, C.J., Christopher Burger, H., Harmeling, S., Scholkopf, B.: A machine learning approach for non-blind image deconvolution. In: CVPR, 2013, pp. 1067–1074
Schuler, C.J., Hirsch, M., Harmeling, S., Schölkopf, B.: Learning to deblur. IEEE T. Pattern Anal. 38(7), 1439–1451 (2016)
Xu, L., Ren, J.S., Liu, C., Jia, J.: Deep convolutional neural network for image deconvolution. In: NIPS, pp. 1790–1798 (2014)
Levin, A.: Blind motion deblurring using image statistics. In: Advances in Neural Information Processing Systems (NIPS) (2007)
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)
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)
Decencière, E., Zhang, X., Cazuguel, G., Laÿ, B., Cochener, B., Trone, C., Gain, P., Ordonez, R., Massin, P., Erginay, A., et al.: Feedback on a publicly distributed image database: the messidor database. Image Anal. Stereol. 33(3), 231–234 (2014)
Acknowledgement
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Williams, B.M. et al. (2017). Fast Blur Detection and Parametric Deconvolution of Retinal Fundus Images. In: Cardoso, M., et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham. https://doi.org/10.1007/978-3-319-67561-9_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-67561-9_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67560-2
Online ISBN: 978-3-319-67561-9
eBook Packages: Computer ScienceComputer Science (R0)