{"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/enhancenet-single-image-super-resolution","title":"EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis","arxiv_id":"1612.07919","date":"2016-12-23","proceeding":"ICCV 2017 10","authors":["Mehdi S. M. Sajjadi","Bernhard Schölkopf","Michael Hirsch"],"abstract":"Single image super-resolution is the task of inferring a high-resolution\nimage from a single low-resolution input. Traditionally, the performance of\nalgorithms for this task is measured using pixel-wise reconstruction measures\nsuch as peak signal-to-noise ratio (PSNR) which have been shown to correlate\npoorly with the human perception of image quality. As a result, algorithms\nminimizing these metrics tend to produce over-smoothed images that lack\nhigh-frequency textures and do not look natural despite yielding high PSNR\nvalues.\n  We propose a novel application of automated texture synthesis in combination\nwith a perceptual loss focusing on creating realistic textures rather than\noptimizing for a pixel-accurate reproduction of ground truth images during\ntraining. By using feed-forward fully convolutional neural networks in an\nadversarial training setting, we achieve a significant boost in image quality\nat high magnification ratios. Extensive experiments on a number of datasets\nshow the effectiveness of our approach, yielding state-of-the-art results in\nboth quantitative and qualitative benchmarks.","url_abs":"http://arxiv.org/abs/1612.07919v2","url_pdf":"http://arxiv.org/pdf/1612.07919v2.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":"enhancenet-single-image-super-resolution","repo_url":"https://github.com/advaza/enhancenet_pretrained","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"enhancenet-single-image-super-resolution","repo_url":"https://github.com/geonm/EnhanceNet-Tensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"enhancenet-single-image-super-resolution","repo_url":"https://github.com/msmsajjadi/EnhanceNet-Code","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"enhancenet-single-image-super-resolution","repo_url":"https://github.com/AniketP04/EnhanceNet-Single-Image-Super-Resolution-Through-Automated-Texture-Synthesis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"super-resolution","task_name":"Super-Resolution"},{"task_slug":"texture-synthesis","task_name":"Texture Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-super-resolution-on-bsd100-4x-upscaling","task":"Image Super-Resolution","dataset":"BSD100 - 4x upscaling","model":"ENet-E","rank_in_archive_order":36,"of":71,"metrics":{"PSNR":"27.50","SSIM":"0.7326"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-ffhq-1024-x-1024-4x","task":"Image Super-Resolution","dataset":"FFHQ 1024 x 1024 - 4x upscaling","model":"EnhanceNet","rank_in_archive_order":5,"of":9,"metrics":{"FID":"19.07","MS-SSIM":"0.934","PSNR":"29.42","SSIM":"0.832"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-ffhq-256-x-256-4x","task":"Image Super-Resolution","dataset":"FFHQ 256 x 256 - 4x upscaling","model":"EnhanceNet","rank_in_archive_order":3,"of":11,"metrics":{"FID":"116.38","MS-SSIM":"0.897","PSNR":"23.64","SSIM":"0.701"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-set14-4x-upscaling","task":"Image Super-Resolution","dataset":"Set14 - 4x upscaling","model":"ENet-E","rank_in_archive_order":66,"of":104,"metrics":{"PSNR":"28.42","SSIM":"0.7774"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-urban100-4x","task":"Image Super-Resolution","dataset":"Urban100 - 4x upscaling","model":"ENet-E","rank_in_archive_order":49,"of":65,"metrics":{"PSNR":"25.66","SSIM":"0.7703"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1612.07919","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1612.07919"}},"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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