{"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/fine-grained-attention-and-feature-sharing","title":"Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-Resolution","arxiv_id":"1911.10773","date":"2019-11-25","proceeding":null,"authors":["Yitong Yan","Chuangchuang Liu","Changyou Chen","Xianfang Sun","Longcun Jin","Xiang Zhou"],"abstract":"The traditional super-resolution methods that aim to minimize the mean square error usually produce the images with over-smoothed and blurry edges, due to the lose of high-frequency details. In this paper, we propose two novel techniques in the generative adversarial networks to produce photo-realistic images for image super-resolution. Firstly, instead of producing a single score to discriminate images between real and fake, we propose a variant, called Fine-grained Attention Generative Adversarial Network for image super-resolution (FASRGAN), to discriminate each pixel between real and fake. FASRGAN adopts a Unet-like network as the discriminator with two outputs: an image score and an image score map. The score map has the same spatial size as the HR/SR images, serving as the fine-grained attention to represent the degree of reconstruction difficulty for each pixel. Secondly, instead of using different networks for the generator and the discriminator in the SR problem, we use a feature-sharing network (Fs-SRGAN) for both the generator and the discriminator. By network sharing, certain information is shared between the generator and the discriminator, which in turn can improve the ability of producing high-quality images. Quantitative and visual comparisons with the state-of-the-art methods on the benchmark datasets demonstrate the superiority of our methods. The application of super-resolution images to object recognition further proves that the proposed methods endow the power to reconstruction capabilities and the excellent super-resolution effects.","url_abs":"https://arxiv.org/abs/1911.10773v2","url_pdf":"https://arxiv.org/pdf/1911.10773v2.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":"fine-grained-attention-and-feature-sharing","repo_url":"https://github.com/Rainyfish/FASRGAN-and-Fs-SRGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"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":"object-recognition","task_name":"Object Recognition"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1911.10773","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}