{"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-adaptive-bilateral-filtering","title":"Fast Adaptive Bilateral Filtering","arxiv_id":"1811.02308","date":"2018-11-06","proceeding":null,"authors":["Ruturaj G. Gavaskar","Kunal. N. Chaudhury"],"abstract":"In the classical bilateral filter, a fixed Gaussian range kernel is used\nalong with a spatial kernel for edge-preserving smoothing. We consider a\ngeneralization of this filter, the so-called adaptive bilateral filter, where\nthe center and width of the Gaussian range kernel is allowed to change from\npixel to pixel. Though this variant was originally proposed for sharpening and\nnoise removal, it can also be used for other applications such as artifact\nremoval and texture filtering. Similar to the bilateral filter, the brute-force\nimplementation of its adaptive counterpart requires intense computations. While\nseveral fast algorithms have been proposed in the literature for bilateral\nfiltering, most of them work only with a fixed range kernel. In this paper, we\npropose a fast algorithm for adaptive bilateral filtering, whose complexity\ndoes not scale with the spatial filter width. This is based on the observation\nthat the concerned filtering can be performed purely in range space using an\nappropriately defined local histogram. We show that by replacing the histogram\nwith a polynomial and the finite range-space sum with an integral, we can\napproximate the filter using analytic functions. In particular, an efficient\nalgorithm is derived using the following innovations: the polynomial is fitted\nby matching its moments to those of the target histogram (this is done using\nfast convolutions), and the analytic functions are recursively computed using\nintegration-by-parts. Our algorithm can accelerate the brute-force\nimplementation by at least $20 \\times$, without perceptible distortions in the\nvisual quality. We demonstrate the effectiveness of our algorithm for\nsharpening, JPEG deblocking, and texture filtering.","url_abs":"http://arxiv.org/abs/1811.02308v1","url_pdf":"http://arxiv.org/pdf/1811.02308v1.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-adaptive-bilateral-filtering","repo_url":"https://github.com/rgavaska/Fast-Adaptive-Bilateral-Filtering","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.02308","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}