[1] M. Amintoosi, M. Fathy and N. Mozayani, Regional varying image super-resolution, In: IEEE international joint conference on computational sciences and optimization, 1, (2009), 913–917.
[2] M. Amintoosi and F. Farbiz, Eigenbackground revisited: can we model the background with eigenvectors?, J. Math. Imaging Vision, 64 (2022), no. 5, 463–477.
[3] M. Amintoosi, Application of Taylor expansion in reducing the size of convolutional neural networks for classifying Impressionism and Miniature paintings, Math. Soc., 5 (2020), no. 1, 1–16. [In Persian]. https://math-sci.ui.ac.ir/article_25351.html.
[4] M. Amintoosi, Fully connected to fully convolutional: road to yesterday, Journal of soft computing and information
technology, 11 (2022), no. 1, 60–72. [In Persian]. https://jscit.nit.ac.ir/article_149453.
[5] M. Amintoosi, Style transfer for data augmentation in convolutional neural networks applied to fire detection, Computational intelligence in electrical engineering, 13 (2022), no. 4, 97–114. [In Persian]. https://isee.ui.ac.ir/article_26042.html.
[6] T. Y. Chiu and D. Gurari, PCA-based knowledge distillation towards lightweight and content-style balanced photorealistic style transfer models, In: 2022 IEEE/CVF conference on computer vision and pattern recognition (CVPR), Los alamitos, CA, USA: IEEE Computer Society, (2022), 7834–7843, https: //doi.ieeecomputersociety.org/10.1109/CVPR52688.2022.00769.
[7] Y. Chen, Y. K. Lai and Y. J. Liu, CartoonGAN: generative adversarial networks for photo cartoonization, In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), (2018), 9465–9474. https://doi.org/10.1109/CVPR.2018.00986.
[8] J. Chung, S. Hyun and J. P. Heo, Style injection in diffusion: a training-free approach for adapting largescale diffusion models for style transfer, In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR), (2024), 8795–8805.
[9] L. A. Gatys , A. S. Ecker and M. Bethge, Image style transfer using convolutional neural networks, In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), (2016), 2414–2423.
[10] L. A. Gatys, A. S. Ecker and M. Bethge, Texture synthesis using convolutional neural networks, In: Proceedings of the 29th international conference on neural information processing systems, 1, NIPS’15, Cambridge, MA, USA: MIT Press; (2015), 262–270.
[11] 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 (CVPR), (2016).
[12] X. Huang and S. Belongie, Arbitrary style Transfer in real-time with adaptive instance normalization, In: Proceedings of the IEEE international conference on computer vision (ICCV), (2017), 1510–1519. https://doi.org/10.1109/ICCV.2017.167.
[13] A. Hertzmann, Can computers create art?, Arts, 7 (2018), no. 2, pp. 25. https://www.mdpi.com/2076-0752/7/2/18.
[14] Y. Jing, Y. Yang, Z. Feng, J. Ye, Y. Yu and M. Song, Neural style transfer: a review, IEEE Transactions on Visualization & Computer Graphics, 26 (2020) , no. 11, 3365–3385. https://doi.org/10.48550/arXiv.1705.04058.
[15] Y. Jing, Y. Yang, Z. Feng, J. Ye, Y. Yu and M. Song, Neural style transfer: a review, IEEE transactions on visualization and computer graphics, 26 (2020), no. 11, 3365–3385. https://doi.org/10.1109/TVCG.
[19] Y. T. Kao, H. J. Lin, K. J. Lin and Y. Tokuyama, Content-preserving image style transfer via reversible networks with meta ActNorm, Electronics, 15 (2026), no. 2, 395. https://doi.org/10.3390/electronics15020395.
[20] X. Li, S. Liu, J. Kautz and M. H. Yang, Learning linear transformations for fast image and video style transfer, In: IEEE/CVF conference on computer vision and pattern recognition (CVPR), (2019), 3804–3812.
[21] S. Li, X. Xu, L. Nie and T. S. Chua, Laplacian-steered neural style transfer, In: Proceedings of the 26th international conference on world wide web (WWW), (2017), 1123–1132. https://doi.org/10.1145/3038912.3052617.
[22] J. Redmon and A. Farhadi, YOLO9000: Better, Faster, Stronger, CVPR, 2017.
[23] K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, In: International conference on learning representations, (2015).
[24] A. Singh, V. Jaiswal, G. Joshi, A. Sanjeeve, S. Gite and K. Kotecha, Neural style transfer: a critical review, IEEE Access, 9 (2021) ,60297–60311. https://doi.org/10.1109/ACCESS.2021.3112996.
[25] A. Vidhya, The world through the eyes of CNN, 2020. Accessed: (2025). https://medium.com/analytics-vidhya/the-world-through-the-eyes-of-cnn-5a52c034dbeb.
[26] X. Wang and J. Jiang, Illustration image style transfer method design based on improved cyclic consistent adversarial network, PLOS ONE, 20 (2025), no. 1, e0313113.
[27] J. Yoo, Y. Uh, S. Chun, B.Kang and J. W. Ha, Photorealistic style transfer via wavelet transforms, In: IEEE/CVF international conference on computer vision (ICCV), (2019), 9035–9044.
[28] M. D. Zeiler and R. Fergus, Visualizing and understanding convolutional networks, In: European conference on computer vision (ECCV), Springer, (2014), 818–833.
[29] Y. Zheng, C. Yang and A. Merkulov, Breast cancer screening using convolutional neural network and follow-up digital mammography, In: A. Mahalanobis, A. Ashok, L. Tian and J. C. Petruccelli, editors, Conference: Computational Imaging III, 10669, International society for optics and photonics, SPIE, (2018). https://doi.org/10.1117/12.2304564.
[30] Z. Zou, T. Shi, S. Qiu, Y. Yuan and Z. Shi, Stylized neural painting, In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR), Los Alamitos, CA, USA: IEEE Computer Society, (2021), 15684–15693. https://doi.ieeecomputersociety.org/10.1109/CVPR46437.2021.01543.