{"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/zero-shot-super-resolution-using-deep","title":"\"Zero-Shot\" Super-Resolution using Deep Internal Learning","arxiv_id":"1712.06087","date":"2017-12-17","proceeding":null,"authors":["Assaf Shocher","Nadav Cohen","Michal Irani"],"abstract":"Deep Learning has led to a dramatic leap in Super-Resolution (SR) performance\nin the past few years. However, being supervised, these SR methods are\nrestricted to specific training data, where the acquisition of the\nlow-resolution (LR) images from their high-resolution (HR) counterparts is\npredetermined (e.g., bicubic downscaling), without any distracting artifacts\n(e.g., sensor noise, image compression, non-ideal PSF, etc). Real LR images,\nhowever, rarely obey these restrictions, resulting in poor SR results by SotA\n(State of the Art) methods. In this paper we introduce \"Zero-Shot\" SR, which\nexploits the power of Deep Learning, but does not rely on prior training. We\nexploit the internal recurrence of information inside a single image, and train\na small image-specific CNN at test time, on examples extracted solely from the\ninput image itself. As such, it can adapt itself to different settings per\nimage. This allows to perform SR of real old photos, noisy images, biological\ndata, and other images where the acquisition process is unknown or non-ideal.\nOn such images, our method outperforms SotA CNN-based SR methods, as well as\nprevious unsupervised SR methods. To the best of our knowledge, this is the\nfirst unsupervised CNN-based SR method.","url_abs":"http://arxiv.org/abs/1712.06087v1","url_pdf":"http://arxiv.org/pdf/1712.06087v1.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":"zero-shot-super-resolution-using-deep","repo_url":"https://github.com/Davidwang96/my-ZSSR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"zero-shot-super-resolution-using-deep","repo_url":"https://github.com/HarukiYqM/pytorch-ZSSR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"zero-shot-super-resolution-using-deep","repo_url":"https://github.com/Weifeng73/Zero-Shot-Super-resolution","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"zero-shot-super-resolution-using-deep","repo_url":"https://github.com/assafshocher/ZSSR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"zero-shot-super-resolution-using-deep","repo_url":"https://github.com/galprz/ZSSR-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"zero-shot-super-resolution-using-deep","repo_url":"https://github.com/mohit1997/ZSSR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"zero-shot-super-resolution-using-deep","repo_url":"https://github.com/tuvovan/ZSSR-Keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"image-compression","task_name":"Image Compression"},{"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":"ZSSR","rank_in_archive_order":52,"of":71,"metrics":{"PSNR":"27.12","SSIM":"0.7211"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-set14-4x-upscaling","task":"Image Super-Resolution","dataset":"Set14 - 4x upscaling","model":"ZSSR","rank_in_archive_order":82,"of":104,"metrics":{"PSNR":"28.01","SSIM":"0.7651"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.06087","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}