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Syst."],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Single image super resolution has significantly advanced by utilizing transformers-based deep learning algorithms. However, challenges still need to be addressed in handling grid-like image patches with higher computational demands and addressing issues like over-smoothing in visual patches. This paper presents a Deep Learning model for single-image super-resolution. In this paper, we present the XTNSR model, a novel multi-path network architecture that combines Local feature window transformers (LWFT) with Xception blocks for single-image super-resolution. The model processes grid-like image patches effectively and reduces computational complexity by integrating a Patch Embedding layer. Whereas the Xception blocks use depth-wise separable convolutions for hierarchical feature extraction, the LWFT blocks capture long-range dependencies and fine-grained qualities. A multi-layer feature fusion block with skip connections, part of this hybrid architecture, guarantees efficient local and global feature fusion. The experimental results show better performance in Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual quality than the state-of-the-art techniques. By optimizing parameters, the suggested architecture also lowers computational complexity. Overall, the architecture presents a promising approach for advancing image super-resolution capabilities.<\/jats:p>","DOI":"10.1007\/s40747-024-01760-1","type":"journal-article","created":{"date-parts":[[2025,1,25]],"date-time":"2025-01-25T04:09:09Z","timestamp":1737778149000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["XTNSR: Xception-based transformer network for single image super resolution"],"prefix":"10.1007","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4652-4196","authenticated-orcid":false,"given":"Jagrati","family":"Talreja","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9840-3171","authenticated-orcid":false,"given":"Supavadee","family":"Aramvith","sequence":"additional","affiliation":[]},{"given":"Takao","family":"Onoye","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,25]]},"reference":[{"issue":"1","key":"1760_CR1","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1093\/comjnl\/bxm075","volume":"52","author":"JH Greenspan","year":"2009","unstructured":"Greenspan JH (2009) Super-resolution in medical imaging. 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