{"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/edge-informed-single-image-super-resolution","title":"Edge-Informed Single Image Super-Resolution","arxiv_id":"1909.05305","date":"2019-09-11","proceeding":null,"authors":["Kamyar Nazeri","Harrish Thasarathan","Mehran Ebrahimi"],"abstract":"The recent increase in the extensive use of digital imaging technologies has brought with it a simultaneous demand for higher-resolution images. We develop a novel edge-informed approach to single image super-resolution (SISR). The SISR problem is reformulated as an image inpainting task. We use a two-stage inpainting model as a baseline for super-resolution and show its effectiveness for different scale factors (x2, x4, x8) compared to basic interpolation schemes. This model is trained using a joint optimization of image contents (texture and color) and structures (edges). Quantitative and qualitative comparisons are included and the proposed model is compared with current state-of-the-art techniques. We show that our method of decoupling structure and texture reconstruction improves the quality of the final reconstructed high-resolution image. Code and models available at: https://github.com/knazeri/edge-informed-sisr","url_abs":"https://arxiv.org/abs/1909.05305v1","url_pdf":"https://arxiv.org/pdf/1909.05305v1.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":"edge-informed-single-image-super-resolution","repo_url":"https://github.com/knazeri/edge-informed-sisr","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"edge-informed-single-image-super-resolution","repo_url":"https://github.com/AntonioAlgaida/Edge.SRGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-inpainting","task_name":"Image Inpainting"},{"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/image-super-resolution-on-bsd100-4x-upscaling","task":"Image Super-Resolution","dataset":"BSD100 - 4x upscaling","model":"Edge-informed SR","rank_in_archive_order":68,"of":71,"metrics":{"PSNR":"24.25","SSIM":"0.851"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-celeb-hq-4x","task":"Image Super-Resolution","dataset":"Celeb-HQ 4x upscaling","model":"Edge-informed SR","rank_in_archive_order":1,"of":1,"metrics":{"PSNR":"28.23","SSIM":"0.912"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-set14-4x-upscaling","task":"Image Super-Resolution","dataset":"Set14 - 4x upscaling","model":"Edge-informed SR","rank_in_archive_order":101,"of":104,"metrics":{"PSNR":"25.19","SSIM":"0.894"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}