{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sdt-dcscn-for-simultaneous-super-resolution","title":"SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images","arxiv_id":"2201.05865","date":"2022-01-15","proceeding":null,"authors":["Hala Neji","Mohamed Ben Halima","Javier Nogueras-Iso","Tarek. M. Hamdani","Abdulrahman M. Qahtani","Omar Almutiry","Habib Dhahri","Adel M. ALIMI"],"abstract":"Deep convolutional neural networks (Deep CNN) have achieved hopeful performance for single image super-resolution. In particular, the Deep CNN skip Connection and Network in Network (DCSCN) architecture has been successfully applied to natural images super-resolution. In this work we propose an approach called SDT-DCSCN that jointly performs super-resolution and deblurring of low-resolution blurry text images based on DCSCN. Our approach uses subsampled blurry images in the input and original sharp images as ground truth. The used architecture is consists of a higher number of filters in the input CNN layer to a better analysis of the text details. The quantitative and qualitative evaluation on different datasets prove the high performance of our model to reconstruct high-resolution and sharp text images. In addition, in terms of computational time, our proposed method gives competitive performance compared to state of the art methods.","url_abs":"https://arxiv.org/abs/2201.05865v1","url_pdf":"https://arxiv.org/pdf/2201.05865v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sdt-dcscn-for-simultaneous-super-resolution","repo_url":"https://github.com/halaneji/SDT-DCSCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"deblurring","task_name":"Deblurring"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/super-resolution-on-hradis-et-al-dataset","task":"Super-Resolution","dataset":"hradis et al dataset","model":"super-resolution","rank_in_archive_order":1,"of":1,"metrics":{"Average PSNR":"20.406","SSIM":"0.877"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}