{"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/to-learn-image-super-resolution-use-a-gan-to","title":"To learn image super-resolution, use a GAN to learn how to do image degradation first","arxiv_id":"1807.11458","date":"2018-07-30","proceeding":"ECCV 2018 9","authors":["Adrian Bulat","Jing Yang","Georgios Tzimiropoulos"],"abstract":"This paper is on image and face super-resolution. The vast majority of prior\nwork for this problem focus on how to increase the resolution of low-resolution\nimages which are artificially generated by simple bilinear down-sampling (or in\na few cases by blurring followed by down-sampling).We show that such methods\nfail to produce good results when applied to real-world low-resolution, low\nquality images. To circumvent this problem, we propose a two-stage process\nwhich firstly trains a High-to-Low Generative Adversarial Network (GAN) to\nlearn how to degrade and downsample high-resolution images requiring, during\ntraining, only unpaired high and low-resolution images. Once this is achieved,\nthe output of this network is used to train a Low-to-High GAN for image\nsuper-resolution using this time paired low- and high-resolution images. Our\nmain result is that this network can be now used to efectively increase the\nquality of real-world low-resolution images. We have applied the proposed\npipeline for the problem of face super-resolution where we report large\nimprovement over baselines and prior work although the proposed method is\npotentially applicable to other object categories.","url_abs":"http://arxiv.org/abs/1807.11458v1","url_pdf":"http://arxiv.org/pdf/1807.11458v1.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":"to-learn-image-super-resolution-use-a-gan-to","repo_url":"https://github.com/yoon28/unpaired_face_sr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"to-learn-image-super-resolution-use-a-gan-to","repo_url":"https://github.com/jingyang2017/Face-and-Image-super-resolution","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.11458","atlas_url":"https://app.syntology.ai/?focus=1807.11458","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.11458"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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