{"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/deep-learning-based-image-super-resolution","title":"Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality","arxiv_id":"1809.04789","date":"2018-09-13","proceeding":null,"authors":["Jun-Ho Choi","Jun-Hyuk Kim","Manri Cheon","Jong-Seok Lee"],"abstract":"Recently, it has been shown that in super-resolution, there exists a tradeoff\nrelationship between the quantitative and perceptual quality of super-resolved\nimages, which correspond to the similarity to the ground-truth images and the\nnaturalness, respectively. In this paper, we propose a novel super-resolution\nmethod that can improve the perceptual quality of the upscaled images while\npreserving the conventional quantitative performance. The proposed method\nemploys a deep network for multi-pass upscaling in company with a discriminator\nnetwork and two quantitative score predictor networks. Experimental results\ndemonstrate that the proposed method achieves a good balance of the\nquantitative and perceptual quality, showing more satisfactory results than\nexisting methods.","url_abs":"http://arxiv.org/abs/1809.04789v2","url_pdf":"http://arxiv.org/pdf/1809.04789v2.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":"deep-learning-based-image-super-resolution","repo_url":"https://github.com/idearibosome/tf-perceptual-eusr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"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":[{"leaderboard":"/sota/image-super-resolution-on-bsd100-4x-upscaling","task":"Image Super-Resolution","dataset":"BSD100 - 4x upscaling","model":"4PP-EUSR","rank_in_archive_order":57,"of":71,"metrics":{"PSNR":"26.5707","SSIM":"0.6900"},"uses_additional_data":false},{"leaderboard":"/sota/image-super-resolution-on-set14-4x-upscaling","task":"Image Super-Resolution","dataset":"Set14 - 4x upscaling","model":"4PP-EUSR","rank_in_archive_order":90,"of":104,"metrics":{"PSNR":"27.6222","SSIM":"0.7419"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}