[1] M. D. Abràmoff, M. K. Garvin, and M. Sonka, "Retinal Imaging and Image Analysis," IEEE Reviews in Biomedical Engineering, vol. 3, pp. 169-208, 2010, doi: 10.1109/RBME.2010.2084567.
[2] A. D. Fleming, S. Philip, K. A. Goatman, J. A. Olson, and P. F. Sharp, "Automated Assessment of Diabetic Retinal Image Quality Based on Clarity and Field Definition," Investigative Ophthalmology & Visual Science, vol. 47, no. 3, pp. 1120-1125, 2006, doi: 10.1167/iovs.05-1155.
[3] A. Hoover and M. Goldbaum, "Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels," IEEE Transactions on Medical Imaging, vol. 22, no. 8, pp. 951-958, 2003, doi: 10.1109/TMI.2003.815900.
[4] J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. v. Ginneken, "Ridge-based vessel segmentation in color images of the retina," IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 501-509, 2004, doi: 10.1109/TMI.2004.825627.
[5] E. Gonzalez-Amador, J. Arines, P. Charlón, N. Garcia-Porta, M. J. Abraldes, and E. Acosta, "Improvement of Retinal Images Affected by Cataracts," Photonics, vol. 9, no. 4, p. 251, 2022, doi: 10.3390/photonics9040251.
[6] X. Fang, Y. Wang, X. Li, W. Fan, and D. Zhou, "A two-stage multi-scale attention-based network for weakly supervised cataract fundus image enhancement," Scientific Reports, vol. 15, no. 1, p. 27610, 2025/07/29 2025, doi: 10.1038/s41598-025-12157-6.
[7] A. B. Crane, H. S. Choudhry, and M. H. Dastjerdi, "Effect of simulated cataract on the accuracy of artificial intelligence in detecting diabetic retinopathy in color fundus photos," Indian Journal of Ophthalmology, vol. 72, no. Suppl 1, pp. S42-S45, 2024, doi: 10.4103/ijo.Ijo_1163_23.
[8] L. Xiong, H. Li, and L. Xu, "An Approach to Evaluate Blurriness in Retinal Images with Vitreous Opacity for Cataract Diagnosis," Journal of Healthcare Engineering, vol. 2017, no. 1, p. 5645498, 2017, doi: 10.1155/2017/5645498.
[9] O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Cham, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds., 2015// 2015: Springer International Publishing, pp. 234-241, doi: 10.1007/978-3-319-24574-4_28.
[10] L. Jiao et al., "A Survey of Deep Learning-Based Object Detection," IEEE Access, vol. 7, pp. 128837-128868, 2019, doi: 10.1109/ACCESS.2019.2939201.
[11] F. D. Hernandez-Gutierrez, E. G. Avina-Bravo, M. A. Ibarra-Manzano, J. Ruiz-Pinales, E. Ovalle-Magallanes, and J. G. Avina-Cervantes, "Retinal Vessel Segmentation Based on a Lightweight U-Net and Reverse Attention," Mathematics, vol. 13, no. 13, p. 2203, 2025. doi: 10.3390/math13132203
[12] Z. Gong et al., "Versatile cataract fundus image restoration model utilizing unpaired cataract and high-quality images," Scientific Reports, vol. 15, no. 1, p. 11171, 2025/04/01 2025, doi: 10.1038/s41598-025-88444-z.
[13] E. Peli and T. Peli, "Restoration of retinal images obtained through cataracts," IEEE Transactions on Medical Imaging, vol. 8, no. 4, pp. 401-406, 1989, doi: 10.1109/42.41493.
[14] X. Xu, Y. Guan, J. Li, Z. Ma, L. Zhang, and L. Li, "Automatic glaucoma detection based on transfer induced attention network," BioMedical Engineering OnLine, vol. 20, no. 1, p. 39, 2021/04/23 2021, doi: 10.1186/s12938-021-00877-5.
[15] H. Xie et al., "Deep learning for detecting visually impaired cataracts using fundus images," (in English), Frontiers in Cell and Developmental Biology, Original Research vol. Volume 11 - 2023, 2023-July-28 2023, doi: 10.3389/fcell.2023.1197239.
[16] م. زمانی و ر. حسن زاده, «تشخیص بیماریهای شایع چشمی در تصاویر شبکیه حاوی آبمروارید با استفاده از شبکه یادگیری عمیق»، مجله ماشین بینایی و پردازش تصویر، دوره ۱۰، شماره ۴، صص. ۷۵–۸۴، ۱۴۰۲.
[17] M. F. Marmor, "Ophthalmology and Art: Simulation of Monet's Cataracts and Degas' Retinal Disease," Archives of Ophthalmology, vol. 124, no. 12, pp. 1764-1769, 2006, doi: 10.1001/archopht.124.12.1764.
[18] V. F. Pamplona et al., "CATRA: interactive measuring and modeling of cataracts," ACM Trans. Graph., vol. 30, no. 4, p. Article 47, 2011, doi: 10.1145/2010324.1964942.
[19] K. Jin et al., "FIVES: A Fundus Image Dataset for Artificial Intelligence based Vessel Segmentation," Scientific Data, vol. 9, no. 1, p. 475, 2022/08/04 2022, doi: 10.1038/s41597-022-01564-3.
[20] Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang, "UNet++: A Nested U-Net Architecture for Medical Image Segmentation," Cham, 2018: Springer International Publishing, in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3-11.
[21] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
[22] T. Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, "Feature Pyramid Networks for Object Detection," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 21-26 July 2017 2017, pp. 936-944, doi: 10.1109/CVPR.2017.106.
[23] L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, "Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation," Cham, 2018: Springer International Publishing, in Computer Vision – ECCV 2018, pp. 833-851.
[24] K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," CoRR, vol. abs/1409.1556, 2014.
[25] M. Tan and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," ArXiv, vol. abs/1905.11946, 2019.
[26] M. S. Gargari, M. H. Seyedi, and M. Alilou, "Segmentation of Retinal Blood Vessels Using U-Net++ Architecture and Disease Prediction," Electronics, vol. 11, no. 21, p. 3516, 2022, doi: 10.3390/electronics11213516.
[27] W. Liu, Y. Jiang, J. Zhang, and Z. Ma, "RFARN: Retinal vessel segmentation based on reverse fusion attention residual network," PLOS ONE, vol. 16, no. 12, p. e0257256, 2021, doi: 10.1371/journal.pone.0257256.
[28] Y. Liu, J. Shen, L. Yang, G.-B. Bian, and H. Yu, "ResDO-UNet: A deep residual network for accurate retinal vessel segmentation from fundus images," Biomedical Signal Processing and Control, vol. 79, p. 104087, 2023, doi: 10.1016/j.bspc.2022.104087.
[29] N. Alsharabi, A. Echtioui, A. Alayba, G. Alshammari, and H. Hamam, "Advanced hybrid UNet architectures for retinal vessel segmentation," Egyptian Informatics Journal, vol. 32, p. 100823, 2025, doi: 10.1016/j.eij.2025.100823.
[30] T. Haklı and İ. Zaim Gökbay, "Evaluation of Image Segmentation Models in Retinal Vessel Segmentation," in Medical Informatics IV, N. Bozbuğa and S. Gülseçen Eds.: Istanbul University Press, 2024, pp. 105-112.
[31] M. C. S. Tang, S. S. Teoh, and H. Ibrahim, "Retinal Vessel Segmentation from Fundus Images Using DeepLabv3+," presented at the 2022 IEEE 18th International Colloquium on Signal Processing & Applications (CSPA), 2022, doi: 10.1109/CSPA55076.2022.9781891.
[32] M. R. Mathews, S. M. Anzar, R. Kalesh Krishnan, and A. Panthakkan, "EfficientNet for retinal blood vessel segmentation," presented at the 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS), 2020, doi: 10.1109/ICSPIS51252.2020.9340135.
[33] F. Milletari, N. Navab, and S. A. Ahmadi, "V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation," in 2016 Fourth International Conference on 3D Vision (3DV), 25-28 Oct. 2016 2016, pp. 565-571, doi: 10.1109/3DV.2016.79.
[34] S. Pachade et al., "Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research," Data, vol. 6, no. 2, p. 14, 2021. [Online]. Available: https://www.mdpi.com/2306-5729/6/2/14.
