{"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/isotropic-and-steerable-wavelets-in-n","title":"Isotropic and Steerable Wavelets in N Dimensions. A multiresolution analysis framework for ITK","arxiv_id":"1710.01103","date":"2017-10-03","proceeding":null,"authors":["Pablo Hernandez-Cerdan"],"abstract":"This document describes the implementation of the external module\nITKIsotropicWavelets, a multiresolution (MRA) analysis framework using\nisotropic and steerable wavelets in the frequency domain. This framework\nprovides the backbone for state of the art filters for denoising, feature\ndetection or phase analysis in N-dimensions. It focus on reusability, and\nhighly decoupled modules for easy extension and implementation of new filters,\nand it contains a filter for multiresolution phase analysis,\n  The backbone of the multi-scale analysis is provided by an isotropic\nband-limited wavelet pyramid, and the detection of directional features is\nprovided by coupling the pyramid with a generalized Riesz transform. The\ngeneralized Riesz transform of order N behaves like a smoothed version of the\nNth order derivatives of the signal. Also, it is steerable: its components\nimpulse responses can be rotated to any spatial orientation, reducing\ncomputation time when detecting directional features.","url_abs":"http://arxiv.org/abs/1710.01103v1","url_pdf":"http://arxiv.org/pdf/1710.01103v1.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":"isotropic-and-steerable-wavelets-in-n","repo_url":"https://github.com/InsightSoftwareConsortium/ITKIsotropicWavelets","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"denoising","task_name":"Denoising"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}