{"title":"Regularization of orthonormal vector sets using coupled PDE's","authors":"D. Tschumperlé, R. Deriche","doi":"10.1109/VLSM.2001.938875","DOIUrl":null,"url":null,"abstract":"We address the problem of restoring, while presenting possible discontinuities, fields of noisy orthonormal vector sets, taking the orthonormal constraints explicity into account. We develop a variational solution for the general case where each image feature may correspond to multiple n-D orthogonal vectors of unit norms. We first formulate the problem in a new variational framework, where discontinuities and orthonormal constraints are preserved by means of constrained minimization and /spl Phi/-function regularization, leading to a set of coupled anisotropic diffusion PDE. A geometric interpretation of the resulting equations, coming from the field of solid mechanics, is proposed for the 3D case. Two interesting restrictions of our framework are also tackled: the regularization of 30 rotation matrices and the direction diffusion (the parallel with previous works is made). Finally, we present a number of denoising results and applications.","PeriodicalId":445975,"journal":{"name":"Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"47","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VLSM.2001.938875","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 47
Abstract
We address the problem of restoring, while presenting possible discontinuities, fields of noisy orthonormal vector sets, taking the orthonormal constraints explicity into account. We develop a variational solution for the general case where each image feature may correspond to multiple n-D orthogonal vectors of unit norms. We first formulate the problem in a new variational framework, where discontinuities and orthonormal constraints are preserved by means of constrained minimization and /spl Phi/-function regularization, leading to a set of coupled anisotropic diffusion PDE. A geometric interpretation of the resulting equations, coming from the field of solid mechanics, is proposed for the 3D case. Two interesting restrictions of our framework are also tackled: the regularization of 30 rotation matrices and the direction diffusion (the parallel with previous works is made). Finally, we present a number of denoising results and applications.