1. Z. Gao et al., "Vessel segmentation for X-ray coronary angiography using ensemble methods with deep learning and filter-based features," BMC Medical Imaging, vol. 22, no. 1, p. 10, 2022.
2. P. Gailloud, S. Oishi, and K. Murphy, "Three-dimensional fusion digital subtraction angiography: new reconstruction algorithm for simultaneous three-dimensional rendering of osseous and vascular information obtained during rotational angiography," American journal of neuroradiology, vol. 26, no. 4, pp. 908-911, 2005.
3. W. Quan, J. Chen, Y. Liu, D.-M. Yan, and P. Wonka, "Deep learning-based image and video inpainting: A survey," International Journal of Computer Vision, vol. 132, no. 7, pp. 2367-2400, 2024.
4. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, "Multiscale vessel enhancement filtering," in Medical Image Computing and Computer-Assisted Intervention—MICCAI’98: First International Conference Cambridge, MA, USA, October 11–13, 1998 Proceedings 1, 1998: Springer, pp. 130-137.
5. J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang, "Free-form image inpainting with gated convolution," in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 4471-4480.
6. K. B. Khan, A. A. Khaliq, A. Jalil, and M. Shahid, "A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising," PloS one, vol. 13, no. 2, p. e0192203, 2018.
7. D. Jimenez-Carretero, A. Santos, S. Kerkstra, R. D. Rudyanto, and M. J. Ledesma-Carbayo, "3D Frangi-based lung vessel enhancement filter penalizing airways," in 2013 IEEE 10th international symposium on biomedical imaging, 2013: IEEE, pp. 926-929.
8. C. Barnes, E. Shechtman, A. Finkelstein, and D. B. Goldman, "PatchMatch: A randomized correspondence algorithm for structural image editing," ACM Trans. Graph., vol. 28, no. 3, p. 24, 2009.
9. S. Darabi, E. Shechtman, C. Barnes, D. B. Goldman, and P. Sen, "Image melding: Combining inconsistent images using patch-based synthesis," ACM Transactions on graphics (TOG), vol. 31, no. 4, pp. 1-10, 2012.
10. D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, "Context encoders: Feature learning by inpainting," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2536-2544.
11. S. Iizuka, E. Simo-Serra, and H. Ishikawa, "Globally and locally consistent image completion," ACM Transactions on Graphics (ToG), vol. 36, no. 4, pp. 1-14, 2017.
12. G. Liu, F. A. Reda, K. J. Shih, T.-C. Wang, A. Tao, and B. Catanzaro, "Image inpainting for irregular holes using partial convolutions," in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 85-100.
13. J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang, "Generative image inpainting with contextual attention," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 5505-5514.
14. I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, "Improved training of wasserstein gans," Advances in neural information processing systems, vol. 30, 2017.
15. J. Adler and S. Lunz, "Banach wasserstein gan," Advances in neural information processing systems, vol. 31, 2018.
16. Z. Yi, Q. Tang, S. Azizi, D. Jang, and Z. Xu, "Contextual residual aggregation for ultra high-resolution image inpainting," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 7508-7517.
17. S. Qu, Z. Niu, J. Zhu, B. Dong, and K. Huang, "Structure first detail next: Image inpainting with pyramid generator," in 2023 IEEE International Conference on Multimedia and Expo (ICME), 2023: IEEE, pp. 1265-1270.
18. S. Song et al., "Patch-based adaptive background subtraction for vascular enhancement in X-ray cineangiograms," IEEE journal of biomedical and health informatics, vol. 23, no. 6, pp. 2563-2575, 2019.
19. M. Voorn, U. Exner, and A. Rath, "Multiscale Hessian fracture filtering for the enhancement and segmentation of narrow fractures in 3D image data," Computers & geosciences, vol. 57, pp. 44-53, 2013.
20. S. Tang, Y. Wang, and Y.-W. Chen, "Application of ICA to X-ray coronary digital subtraction angiography," Neurocomputing, vol. 79, pp. 168-172, 2012.
21. Y. Bentoutou and N. Taleb, "Automatic extraction of control points for digital subtraction angiography image enhancement," IEEE Transactions on Nuclear Science, vol. 52, no. 1, pp. 238-246, 2005.
22. M. Nejati, S. Sadri, and R. Amirfattahi, "Nonrigid Image Registration in Digital Subtraction Angiography Using Multilevel B‐Spline," BioMed research international, vol. 2013, no. 1, p. 236315, 2013.
23. H. Ma, A. Hoogendoorn, E. Regar, W. J. Niessen, and T. van Walsum, "Automatic online layer separation for vessel enhancement in X-ray angiograms for percutaneous coronary interventions," Medical image analysis, vol. 39, pp. 145-161, 2017.
24. Z. Liu, P. Luo, X. Wang, and X. Tang, "Large-scale celebfaces attributes (celeba) dataset," Retrieved August, vol. 15, no. 2018, p. 11, 2018.
25. B. Zhou, A. Khosla, A. Lapedriza, A. Torralba, and A. Oliva, "Places: An image database for deep scene understanding," arXiv preprint arXiv:1610.02055, 2016.
26. H. Zhao, O. Gallo, I. Frosio, and J. Kautz, "Loss functions for image restoration with neural networks," IEEE Transactions on computational imaging, vol. 3, no. 1, pp. 47-57, 2016.
27. S. Shakeri and F. Almasganj, "Online tree-structure-constrained RPCA for background subtraction of X-ray coronary angiography images," Computer Methods and Programs in Biomedicine, vol. 258, p. 108463, 2025.
28. H. Ma et al., "Layer separation for vessel enhancement in interventional X-ray angiograms using morphological filtering and robust PCA," in Augmented Environments for Computer-Assisted Interventions: 10th International Workshop, AE-CAI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 9, 2015. Proceedings 10, 2015: Springer, pp. 104-113.
29. R. Chen, Y. Tong, J. Yang, and M. Wu, "Video foreground detection algorithm based on fast principal component pursuit and motion saliency," Computational intelligence and neuroscience, vol. 2019, no. 1, p. 4769185, 2019.
30. X. Ye, J. Yang, X. Sun, K. Li, C. Hou, and Y. Wang, "Foreground–background separation from video clips via motion-assisted matrix restoration," IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 11, pp. 1721-1734, 2015.
31. A. Sobral, S. Javed, S. Ki Jung, T. Bouwmans, and E.-h. Zahzah, "Online stochastic tensor decomposition for background subtraction in multispectral video sequences," in Proceedings of the IEEE International Conference on Computer Vision Workshops, 2015, pp. 106-113.
32. X. Zhou, C. Yang, and W. Yu, "Moving object detection by detecting contiguous outliers in the low-rank representation," IEEE transactions on pattern analysis and machine intelligence, vol. 35, no. 3, pp. 597-610, 2012.
33. D. M. Powers, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation," arXiv preprint arXiv:2010.16061, 2020.
34. Z. Wang, E. P. Simoncelli, and A. C. Bovik, "Multiscale structural similarity for image quality assessment," in The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, 2003, vol. 2: Ieee, pp. 1398-1402.
