University of Sistan and Baluchestan Iranian Journal of Fuzzy Systems 1735-0654 23 4 2026 08 01 FAUNet: A Fuzzy-Attention U-Net for Diffusion-Based Persian Text Image Super-Resolution 55 67 9981 10.22111/ijfs.2026.53720.9515 EN Mahsham Kushki Graduate university of advanced technology Esmat Rashedi Graduate university of advanced technology Elham Shabaninia Graduate university of advanced technology Mehdi Kamandar Graduate university of advanced technology Journal Article 2025 10 31 The accurate enhancement of text images is a critical challenge in computer vision, particularly for languages such as Persian that exhibit complex writing structures, cursive connections, and fine-grained diacritical marks. Traditional super-resolution approaches often fail to preserve these delicate textual details. Here, diffusion model is adopted for text image super-resolution. The U-Net framework of this method is enhanced by incorporating fuzzy logic and attention mechanism (named FAUNet) to address mentioned problems. At the bottleneck of the network, a fuzzy layer is employed to softly model uncertainties and boundary variations, while a spatial channel attention block adaptively emphasizes crucial regions of the image. Together, these components strengthen the network’s capacity to capture structural dependencies and semantic details essential for text clarity. The proposed model is rigorously evaluated on two large-scale Persian text datasets: IR-LPR that comprising vehicle license plate images, and IDPL-PFOD2 that is a dataset of printed Persian text. Experimental results show that FAUNet outperforms state-of-the-art methods achieving improvements in PSNR, SSIM, and MS-SSIM metrics. These improvements not only contribute to higher visual quality but also hold strong potential for downstream applications such as optical character recognition (OCR), license plate recognition, and digital document restoration in low-quality imaging conditions. https://ijfs.usb.ac.ir/article_9981_5da472977c5d69a9da02e954c4f36a7d.pdf