{"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/fast-high-dimensional-bilateral-and-nonlocal","title":"Fast High-Dimensional Bilateral and Nonlocal Means Filtering","arxiv_id":"1811.02363","date":"2018-11-06","proceeding":null,"authors":["Pravin Nair","Kunal. N. Chaudhury"],"abstract":"Existing fast algorithms for bilateral and nonlocal means filtering mostly\nwork with grayscale images. They cannot easily be extended to high-dimensional\ndata such as color and hyperspectral images, patch-based data, flow-fields,\netc. In this paper, we propose a fast algorithm for high-dimensional bilateral\nand nonlocal means filtering. Unlike existing approaches, where the focus is on\napproximating the data (using quantization) or the filter kernel (via analytic\nexpansions), we locally approximate the kernel using weighted and shifted\ncopies of a Gaussian, where the weights and shifts are inferred from the data.\nThe algorithm emerging from the proposed approximation essentially involves\nclustering and fast convolutions, and is easy to implement. Moreover, a variant\nof our algorithm comes with a guarantee (bound) on the approximation error,\nwhich is not enjoyed by existing algorithms. We present some results for\nhigh-dimensional bilateral and nonlocal means filtering to demonstrate the\nspeed and accuracy of our proposal. Moreover, we also show that our algorithm\ncan outperform state-of-the-art fast approximations in terms of accuracy and\ntiming.","url_abs":"http://arxiv.org/abs/1811.02363v1","url_pdf":"http://arxiv.org/pdf/1811.02363v1.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":"fast-high-dimensional-bilateral-and-nonlocal","repo_url":"https://github.com/pravin1390/FastHDFilter","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"quantization","task_name":"Quantization"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}