{"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/u-finger-multi-scale-dilated-convolutional","title":"U-Finger: Multi-Scale Dilated Convolutional Network for Fingerprint Image Denoising and Inpainting","arxiv_id":"1807.10993","date":"2018-07-29","proceeding":null,"authors":["Ramakrishna Prabhu","Xiaojing Yu","Zhangyang Wang","Ding Liu","Anxiao","Jiang"],"abstract":"This paper studies the challenging problem of fingerprint image denoising and\ninpainting. To tackle the challenge of suppressing complicated artifacts (blur,\nbrightness, contrast, elastic transformation, occlusion, scratch, resolution,\nrotation, and so on) while preserving fine textures, we develop a multi-scale\nconvolutional network, termed U- Finger. Based on the domain expertise, we show\nthat the usage of dilated convolutions as well as the removal of padding have\nimportant positive impacts on the final restoration performance, in addition to\nmulti-scale cascaded feature modules. Our model achieves the overall ranking of\nNo.2 in the ECCV 2018 Chalearn LAP Inpainting Competition Track 3 (Fingerprint\nDenoising and Inpainting). Among all participating teams, we obtain the MSE of\n0.0231 (rank 2), PSNR 16.9688 dB (rank 2), and SSIM 0.8093 (rank 3) on the\nhold-out testing set.","url_abs":"http://arxiv.org/abs/1807.10993v2","url_pdf":"http://arxiv.org/pdf/1807.10993v2.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":"u-finger-multi-scale-dilated-convolutional","repo_url":"https://github.com/rgsl888/U-Finger-A-Fingerprint-Denosing-Network","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"image-denoising","task_name":"Image Denoising"},{"task_slug":"ssim","task_name":"SSIM"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}