{"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/meta-sr-a-magnification-arbitrary-network-for","title":"Meta-SR: A Magnification-Arbitrary Network for Super-Resolution","arxiv_id":"1903.00875","date":"2019-03-03","proceeding":"CVPR 2019 6","authors":["Xuecai Hu","Haoyuan Mu","Xiangyu Zhang","Zilei Wang","Tieniu Tan","Jian Sun"],"abstract":"Recent research on super-resolution has achieved great success due to the\ndevelopment of deep convolutional neural networks (DCNNs). However,\nsuper-resolution of arbitrary scale factor has been ignored for a long time.\nMost previous researchers regard super-resolution of different scale factors as\nindependent tasks. They train a specific model for each scale factor which is\ninefficient in computing, and prior work only take the super-resolution of\nseveral integer scale factors into consideration. In this work, we propose a\nnovel method called Meta-SR to firstly solve super-resolution of arbitrary\nscale factor (including non-integer scale factors) with a single model. In our\nMeta-SR, the Meta-Upscale Module is proposed to replace the traditional upscale\nmodule. For arbitrary scale factor, the Meta-Upscale Module dynamically\npredicts the weights of the upscale filters by taking the scale factor as input\nand use these weights to generate the HR image of arbitrary size. For any\nlow-resolution image, our Meta-SR can continuously zoom in it with arbitrary\nscale factor by only using a single model. We evaluated the proposed method\nthrough extensive experiments on widely used benchmark datasets on single image\nsuper-resolution. The experimental results show the superiority of our\nMeta-Upscale.","url_abs":"http://arxiv.org/abs/1903.00875v4","url_pdf":"http://arxiv.org/pdf/1903.00875v4.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":"meta-sr-a-magnification-arbitrary-network-for","repo_url":"https://github.com/Lornatang/Meta_RDN-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"meta-sr-a-magnification-arbitrary-network-for","repo_url":"https://github.com/XuecaiHu/Meta-SR-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.00875","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.00875"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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