{"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/generative-adversarial-network-based-image","title":"Generative adversarial network-based image super-resolution using perceptual content losses","arxiv_id":"1809.04783","date":"2018-09-13","proceeding":null,"authors":["Manri Cheon","Jun-Hyuk Kim","Jun-Ho Choi","Jong-Seok Lee"],"abstract":"In this paper, we propose a deep generative adversarial network for\nsuper-resolution considering the trade-off between perception and distortion.\nBased on good performance of a recently developed model for super-resolution,\ni.e., deep residual network using enhanced upscale modules (EUSR), the proposed\nmodel is trained to improve perceptual performance with only slight increase of\ndistortion. For this purpose, together with the conventional content loss,\ni.e., reconstruction loss such as L1 or L2, we consider additional losses in\nthe training phase, which are the discrete cosine transform coefficients loss\nand differential content loss. These consider perceptual part in the content\nloss, i.e., consideration of proper high frequency components is helpful for\nthe trade-off problem in super-resolution. The experimental results show that\nour proposed model has good performance for both perception and distortion, and\nis effective in perceptual super-resolution applications.","url_abs":"http://arxiv.org/abs/1809.04783v2","url_pdf":"http://arxiv.org/pdf/1809.04783v2.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":"generative-adversarial-network-based-image","repo_url":"https://github.com/manricheon/eusr-pcl-tf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","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":"discrete-cosine-transform","method_name":"Discrete Cosine Transform"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}