{"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/mr-image-super-resolution-with-squeeze-and","title":"MR Image Super-Resolution With Squeeze and Excitation Reasoning Attention Network","arxiv_id":null,"date":"2021-06-19","proceeding":"CVPR 2021 1","authors":["Yulun Zhang","Kai Li","Kunpeng Li","Yun Fu"],"abstract":"    High-quality high-resolution (HR) magnetic resonance (MR) images afford more detailed information for reliable diagnosis and quantitative image analyses. Deep convolutional neural networks (CNNs) have shown promising ability for MR image super-resolution (SR) given low-resolution (LR) MR images. The LR MR images usually share some visual characteristics: repeating patterns, relatively simpler structures, and less informative background. Most previous CNN-based SR methods treat the spatial pixels (including the background) equally. They also fail to sense the entire space of the input, which is critical for high-quality MR image SR. To address those problems, we propose squeeze and excitation reasoning attention networks (SERAN) for accurate MR image SR. We propose to squeeze attention from global spatial information of the input and obtain global descriptors. Such global descriptors enhance the network's ability to focus on more informative regions and structures in MR images. We further build relationship among those global descriptors and propose primitive relationship reasoning attention. The global descriptors are further refined with the learned attention. To fully make use of the aggregated information, we adaptively recalibrate feature responses with learned adaptive attention vectors. These attention vectors select a subset of global descriptors to complement each spatial location for accurate details and texture reconstruction. We propose squeeze and excitation attention with residual scaling, which not only stabilizes the training but also makes it flexible to other basic networks. Extensive experiments show the effectiveness of our proposed SERAN, which clearly surpasses state-of-the-art methods on benchmarks quantitatively and qualitatively.    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_MR_Image_Super-Resolution_With_Squeeze_and_Excitation_Reasoning_Attention_Network_CVPR_2021_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_MR_Image_Super-Resolution_With_Squeeze_and_Excitation_Reasoning_Attention_Network_CVPR_2021_paper.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":[],"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-ixi","task":"Image Super-Resolution","dataset":"IXI","model":"SERAN","rank_in_archive_order":2,"of":9,"metrics":{"PSNR 2x T2w":"40.30","PSNR 4x T2w":"32.62","SSIM 4x T2w":"0.9472","SSIM for 2x T2w":"0.9874"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}