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Learning to Deblur Adaptive Optics Retinal Images

Lazareva, A., Asad, M. & Slabaugh, G. G. (2017). Learning to Deblur Adaptive Optics Retinal Images. Paper presented at the 14th International Conference, ICIAR 2017, 5-7 Jul 2017, Montreal, Canada.


In this paper we propose a blind deconvolution approach for reconstruction of Adaptive Optics (AO) high-resolution retinal images. The framework employs Random Forest to learn the mapping of retinal images onto the space of blur kernels expressed in terms of Zernike coefficients. A specially designed feature extraction technique allows inference of blur kernels for retinal images of various quality, taken at different locations of the retina. This model is validated on synthetically generated images as well as real AO high-resolution retinal images. The obtained results on the synthetic data showed an average root-mean-square error of 0.0051 for the predicted blur kernels and 0.0464 for the reconstructed images, compared to the ground truth (GT). The assessment of the reconstructed AO retinal images demonstrated that the contrast, sharpness and visual quality of the images have been significantly improved.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: Published in final form as Lazareva, A., Asad, M., and Slabaugh, G. (2017) 'Learning to Deblur Adaptive Optics Retinal Images', LNCS, 10317, pp497-507. The final publication is available at Springer via
Publisher Keywords: Adaptive Optics imaging, deconvolution, image restoration, regression, Random Forest
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RE Ophthalmology
Departments: School of Science & Technology > Engineering
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