FAUNet: A Fuzzy-Attention U-Net for Diffusion-Based Persian Text Image Super-Resolution

[1] A. Afkari-Fahandari, F. Asadi-Zeydabadi, E. Shabaninia, H. Nezamabadi-Pour, Enhancing Farsi text recognition
via iteratively using a language model, in 2024 20th CSI International Symposium on Artificial Intelligence and
Signal Processing (AISP), (2024), 1-6. https://doi.org/10.1109/AISP61396.2024.10475269

[2] S. AlHalawani, B. Benjdira, A. Ammar, A. Koubaa, A. M. Ali, DiffPlate: A diffusion model for super-resolution
of license plate images, Electronics, 13(13) (2024), 2670. https://doi.org/10.3390/electronics13132670

[3] F. Asadi-Zeydabadi, A. Afkari-Fahandari, A. Faraji, E. Shabaninia, H. Nezamabadi-Pour, IDPL-PFOD2: A new
large-scale dataset for printed Farsi optical character recognition, arXiv preprint, arXiv:2312.01177, (2023). https:
//doi.org/10.48550/arXiv.2312.01177

[4] A. Brock, J. Donahue, K. Simonyan, Large scale GAN training for high fidelity natural image synthesis, in Proceedings
of the 7th International Conference on Learning Representations (ICLR), 2019. https://doi.org/10.
48550/arXiv.1809.11096

[5] W. Chao, J. Zhao, F. Duan, G. Wang, et al., LFSRDiff: Light field image super-resolution via diffusion models, in
ICASSP 2025–2025, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (2025),
1-5. https://doi.org/10.1109/ICASSP49660.2025.10889642

[6] K. Cheng, et al., Effective diffusion transformer architecture for image super-resolution, in Proceedings of the AAAI
Conference on Artificial Intelligence, 39(3) (2025), 2455-2463. https://doi.org/10.1609/aaai.v39i3.32247

[7] C. Dong, C. C. Loy, K. He, X. Tang, Image super-resolution using deep convolutional networks, IEEE Transactions
on Pattern Analysis and Machine Intelligence, 38(2) (2016), 295-307. https://doi.org/10.1109/TPAMI.2015.
2439281

[8] C. Dong, C. C. Loy, X. Tang, Accelerating the super-resolution convolutional neural network, in European Conference
on Computer Vision, (2016), 391-407. https://doi.org/10.1007/978-3-319-46475-6_25

[9] M. El Helou, Fuzzy-conditioned diffusion and diffusion projection attention applied to facial image correction, in
2023, IEEE International Conference on Image Processing (ICIP), (2023), 236-240. https://doi.org/10.1109/
ICIP49359.2023.10223103

[10] C. Y. Fang, C. S. Fuh, P. Yen, S. Cherng, S. W. Chen, An automatic road sign recognition system based on a
computational model of human recognition processing, Computer Vision and Image Understanding, 96(2) (2004),
237-268. https://doi.org/10.1016/j.cviu.2004.02.007

[11] S. Frolov, T. Hinz, F. Raue, J. Hees, A. Dengel, Adversarial text-to-image synthesis: A review, Neural Networks,
144 (2021), 187-209. https://doi.org/10.1016/j.neunet.2021.07.019

[12] I. J. Goodfellow, et al., Generative adversarial nets, Advances in Neural Information Processing Systems, 27 (2014),
2672-2680. https://doi.org/10.48550/arXiv.1406.2661

[13] M. H. Guo, et al., Attention mechanisms in computer vision: A survey, Computational Visual Media, 8(3) (2022),
331-368. https://doi.org/10.1007/s41095-022-0271-y

[14] J. Ho, A. Jain, P. Abbeel, Denoising diffusion probabilistic models, Advances in Neural Information Processing
Systems, 33 (2020), 6840-6851. https://doi.org/10.48550/arXiv.2006.11239

[15] L. Hua, et al., Attention in diffusion model: A survey, arXiv preprint, arXiv:2504.03738, (2025). https://doi.
org/10.48550/arXiv.2504.03738

[16] R. Keys, Cubic convolution interpolation for digital image processing, IEEE Transactions on Acoustics, Speech,
and Signal Processing, 29(6) (1981), 1153-1160. https://doi.org/10.1109/TASSP.1981.1163711

[17] T. R. Khalifa, X. Yu, X. Zhong, Z. Wu, Adaptive general type-2 fuzzy model-based control for nonlinear networked
systems with packet dropouts, ISA Transactions, 159 (2025), 257-277. https://doi.org/10.1016/j.isatra.2025.
02.009

[18] T. R. Khalifa, X. Yu, X. Zhong, Z. Wu, Indirect adaptive interval type-3 fuzzy tracking control for nonlinear discretetime
networked control systems with DoS attacks, IEEE Transactions on Cybernetics, 55(10) (2025), 4967-4980.
https://doi.org/10.1109/TCYB.2025.3591555

[19] E. Khodadadi, H. R. Kanan, Which super-resolution algorithm is proper for Farsi text image sequences, in 2015
2nd, International Conference on Pattern Recognition and Image Analysis (IPRIA), (2015), 1-4. https://doi.
org/10.1109/PRIA.2015.7161617

[20] D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, in Proceedings of the 3rd International Conference
on Learning Representations (ICLR), (2015). https://doi.org/10.48550/arXiv.1412.6980

[21] M. Kushki, E. Rashedi, E. Shabaninia, M. Kamandar, Enhancing low-resolution Persian license plates via diffusion
models, Journal of Computing and Security, under revision, 2026

[22] C. Ledig, et al., Photo-realistic single image super-resolution using a generative adversarial network, in Proceedings
of the IEEE Conference on Computer Vision and Pattern Recognition, (2017), 4681-4690. https://doi.org/10.
1109/CVPR.2017.19

[23] H. Li, et al., SRDiff: Single image super-resolution with diffusion probabilistic models, Neurocomputing, 479
(2022), 47-59. https://doi.org/10.1016/j.neucom.2022.01.029

[24] J. Liang, J. Cao, G. Sun, K. Zhang, L. Van Gool, R. Timofte, SwinIR: Image restoration using swin transformer,
in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), (2021),
1833-1844. https://doi.org/10.1109/ICCVW54120.2021.00210

[25] Z. Luo, F. K. Gustafsson, Z. Zhao, J. Sjölund, T. B. Schön, Refusion: Enabling large-size realistic image restoration
with latent-space diffusion models, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition Workshops (CVPRW), (2023), 1680-1691. https://doi.org/10.1109/CVPRW59228.2023.00169

[26] K. Mehregan, A. Ahmadyfard, H. Khosravi, Super-resolution of license-plates using frames of low-resolution video,
in 2019 5th, Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), (2019), 1-6. https://doi.
org/10.1109/ICSPIS48872.2019.9066104

[27] B. B. Moser, S. Frolov, F. Raue, S. Palacio, A. Dengel, Dynamic attention-guided diffusion for image superresolution,
in 2025, IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), (2025), 451-460.
https://doi.org/10.1109/WACV61041.2025.00054

[28] B. B. Moser, A. Shanbhag, F. Raue, S. Frolov, S. Palacio, A. Dengel, Diffusion models, image super-resolution and
everything: A survey, IEEE Transactions on Neural Networks and Learning Systems, early access, 36(7) (2025).
https://doi.org/10.1109/TNNLS.2024.3476671

[29] J. Nam, H. Kim, D. Lee, S. Jin, S. Kim, S. Chang, DreamMatcher: Appearance matching self-attention for
semantically-consistent text-to-image personalization, in Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition, (2024), 8100-8110. https://doi.org/10.1109/CVPR52733.2024.00774

[30] Z. Niu, G. Zhong, H. Yu, A review on the attention mechanism of deep learning, Neurocomputing, 452 (2021),
48-62. https://doi.org/10.1016/j.neucom.2021.03.091

[31] M. Rahmani, M. Sabaghian, S. M. Moghadami, M. M. Talaie, M. Naghibi, M. A. Keyvanrad, IR-LPR: A large
scale Iranian license plate recognition dataset, in 2022 12th, International Conference on Computer and Knowledge
Engineering (ICCKE), (2022), 53-58. https://doi.org/10.1109/ICCKE57176.2022.9960129

[32] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, B. Ommer, High-resolution image synthesis with latent diffusion
models, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2022), 10684-
10695. https://doi.org/10.1109/CVPR52688.2022.01042

[33] C. Saharia, J. Ho, W. Chan, T. Salimans, D. J. Fleet, M. Norouzi, Image super-resolution via iterative refinement,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4) (2023), 4713-4726. https://doi.org/10.
1109/TPAMI.2022.3204461

[34] A. Sharafian, A. Ali, I. Ullah, T. R. Khalifa, X. Bai, L. Qiu, Fuzzy adaptive control for consensus tracking in
multiagent systems with incommensurate fractional-order dynamics: Application to power systems, Information
Sciences, 689 (2025), Article 121455. https://doi.org/10.1016/j.ins.2024.121455

[35] W. Shi, et al., Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural
network, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016), 1874-1883.
https://doi.org/10.1109/CVPR.2016.207

[36] J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, S. Ganguli, Deep unsupervised learning using nonequilibrium
thermodynamics, in Proceedings of the 32nd International Conference on Machine Learning (ICML), (2015), 2256-
2265. https://doi.org/10.48550/arXiv.1503.03585

[37] A. Torkian, P. Moallem, Multi-frame super resolution for improving vehicle licence plate recognition, Signal and
Data Processing, 16(2) (2016), 61-76. https://doi.org/10.29252/jsdp.16.2.61

[38] Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli, Image quality assessment: From error visibility to structural
similarity, IEEE Transactions on Image Processing, 13(4) (2004), 600-612. https://doi.org/10.1109/TIP.2003.
819861

[39] Z. Wang, J. Chen, S. C. Hoi, Deep learning for image super-resolution: A survey, IEEE Transactions on Pattern
Analysis and Machine Intelligence, 43(10) (2021), 3365-3387. https://doi.org/10.1109/TPAMI.2020.2982166

[40] Z. Wang, E. P. Simoncelli, A. C. Bovik, Multiscale structural similarity for image quality assessment, in The
Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2 (2003), 1398-1402. https://doi.org/
10.1109/ACSSC.2003.1292216

[41] X. Wang, L. Xie, C. Dong, Y. Shan, Real-ESRGAN: Training real-world blind super-resolution with pure synthetic
data, in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), (2021),
1905-1914. https://doi.org/10.1109/ICCVW54120.2021.00217

[42] Y. Wang, J. Xu, Z. Liang, Q. Chong, X. Cheng, FDM: Document image seen-through removal via fuzzy diffusion
models, Pattern Recognition Letters, 184 (2024), 183-189. https://doi.org/10.1016/j.patrec.2024.06.015

[43] Y. Xiao, Q. Yuan, K. Jiang, J. He, X. Jin, L. Zhang, EDiffSR: An efficient diffusion probabilistic model for
remote sensing image super-resolution, IEEE Transactions on Geoscience and Remote Sensing, 62 (2024), 1-14.
https://doi.org/10.1109/TGRS.2023.3341437

[44] Y. Xue, K. Chen, F. Neri, Differentiable architecture search with attention mechanisms for generative adversarial
networks, IEEE Transactions on Emerging Topics in Computational Intelligence, 8(4) (2024), 3141-3151. https:
//doi.org/10.1109/TETCI.2024.3369998

[45] Y. Xue, X. Han, Z. Wang, Self-adaptive weight based on dual-attention for differentiable neural architecture search,
IEEE Transactions on Industrial Informatics, 20(4) (2024), 6394-6403. https://doi.org/10.1109/TII.2023.
3348843