{"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/fourier-net-fast-image-registration-with-band","title":"Fourier-Net: Fast Image Registration with Band-limited Deformation","arxiv_id":"2211.16342","date":"2022-11-29","proceeding":null,"authors":["Xi Jia","Joseph Bartlett","Wei Chen","Siyang Song","Tianyang Zhang","Xinxing Cheng","Wenqi Lu","Zhaowen Qiu","Jinming Duan"],"abstract":"Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric image data, this process is however resource-intensive and time-consuming. To tackle this problem, we propose the Fourier-Net, replacing the expansive path in a U-Net style network with a parameter-free model-driven decoder. Specifically, instead of our Fourier-Net learning to output a full-resolution displacement field in the spatial domain, we learn its low-dimensional representation in a band-limited Fourier domain. This representation is then decoded by our devised model-driven decoder (consisting of a zero padding layer and an inverse discrete Fourier transform layer) to the dense, full-resolution displacement field in the spatial domain. These changes allow our unsupervised Fourier-Net to contain fewer parameters and computational operations, resulting in faster inference speeds. Fourier-Net is then evaluated on two public 3D brain datasets against various state-of-the-art approaches. For example, when compared to a recent transformer-based method, named TransMorph, our Fourier-Net, which only uses 2.2\\% of its parameters and 6.66\\% of the multiply-add operations, achieves a 0.5\\% higher Dice score and an 11.48 times faster inference speed. Code is available at \\url{https://github.com/xi-jia/Fourier-Net}.","url_abs":"https://arxiv.org/abs/2211.16342v2","url_pdf":"https://arxiv.org/pdf/2211.16342v2.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":"fourier-net-fast-image-registration-with-band","repo_url":"https://github.com/xi-jia/fourier-net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-registration","task_name":"Image Registration"},{"task_slug":"medical-image-registration","task_name":"Medical Image Registration"},{"task_slug":"unsupervised-image-registration","task_name":"Unsupervised Image Registration"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/medical-image-registration-on-oasis","task":"Medical Image Registration","dataset":"OASIS","model":"Fourier-Net","rank_in_archive_order":8,"of":8,"metrics":{"val dsc":"84.7"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2211.16342","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}