S. Kar and S. Maity, Detection of neovascularization in retinal images using mutual information maximization, Computers & Electrical Engineering, vol.62, pp.1-15, 2017.
DOI : 10.1016/j.compeleceng.2017.05.012

J. Tan, H. Fujita, S. Sivaprasad, S. Bhandary, A. Rao et al., Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network, Information Sciences, vol.420, pp.66-76, 2017.
DOI : 10.1016/j.ins.2017.08.050

A. Floriano, Á. Santiago, O. Nieto, and C. Márquez, A machine learning approach to medical image classification: Detecting age-related macular degeneration in fundus images, Computers & Electrical Engineering, 2017.

J. Medhi and S. Dandapat, An effective fovea detection and automatic assessment of diabetic maculopathy in color fundus images, Computers in Biology and Medicine, vol.74, pp.30-44, 2016.
DOI : 10.1016/j.compbiomed.2016.04.007

J. Cheng, J. Liu, Y. Xu, F. Yin, D. Wong et al., Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening, IEEE Transactions on Medical Imaging, vol.32, issue.6, pp.1019-1032, 2013.
DOI : 10.1109/TMI.2013.2247770

S. Devi, K. Ramachandran, and A. Sharma, Retinal Vasculature Segmentation in Smartphone Ophthalmoscope Images, Proceedings of 7th WACBE World Congress on Bioengineering, pp.64-67, 2015.
DOI : 10.1007/978-3-319-19452-3_18

M. Blanckenberg, C. Worst, and C. Scheffer, Development of a mobile phone based ophthalmoscope for telemedicine, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.5236-5239, 2011.
DOI : 10.1109/IEMBS.2011.6091295

S. Wang, K. Jin, H. Lu, C. Cheng, J. Ye et al., Human Visual System-Based Fundus Image Quality Assessment of Portable Fundus Camera Photographs, IEEE Transactions on Medical Imaging, vol.35, issue.4
DOI : 10.1109/TMI.2015.2506902

A. Russo, F. Morescalchi, C. Costagliola, L. Delcassi, and F. Semeraro, A Novel Device to Exploit the Smartphone Camera for Fundus Photography, Journal of Ophthalmology, vol.2015, 2015.
DOI : 10.1016/j.ophtha.2005.12.025
URL : http://doi.org/10.1155/2015/823139

A. Russo, F. Morescalchi, C. Costagliola, L. Delcassi, and F. Semeraro, Comparison of Smartphone Ophthalmoscopy With Slit-Lamp Biomicroscopy for Grading Diabetic Retinopathy, American Journal of Ophthalmology, vol.159, issue.2, pp.360-364, 2015.
DOI : 10.1016/j.ajo.2014.11.008

M. Dobe?, L. Machala, and T. Fürst, Blurred image restoration: A fast method of finding the motion length and angle, Digital Signal Processing, vol.20, issue.6, pp.1677-1686, 2010.
DOI : 10.1016/j.dsp.2010.03.012

J. Cai, H. Ji, C. Liu, and Z. Shen, Blind motion deblurring using multiple images, Journal of Computational Physics, vol.228, issue.14, pp.5057-5071, 2009.
DOI : 10.1016/j.jcp.2009.04.022
URL : http://www.math.nus.edu.sg/%7Ematjh/depository/JCP_2009_deblur.pdf

A. Deshpande and S. Patnaik, Single image motion deblurring: An accurate PSF estimation and ringing reduction, Optik - International Journal for Light and Electron Optics, vol.125, issue.14, pp.3612-3618, 2014.
DOI : 10.1016/j.ijleo.2014.01.126

H. Lidong, Z. Wei, W. Jun, and S. Zebin, Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement, IET Image Processing, vol.9, issue.10, pp.908-915, 2015.
DOI : 10.1049/iet-ipr.2015.0150

M. E. Gegundez-arias, D. Marin, J. M. Bravo, and A. Suero, Locating the fovea center position in digital fundus images using thresholding and feature extraction techniques, Computerized Medical Imaging and Graphics, vol.37, issue.5-6
DOI : 10.1016/j.compmedimag.2013.06.002