Orthonormal vector sets regularization with PDE’s and applications. (English) Zbl 1012.68786
Summary: We are interested in regularizing fields of orthonormal vector sets, using constraint-preserving anisotropic diffusion PDE’s. Each point of such a field is defined by multiple orthogonal and unitary vectors and can indeed represent a lot of interesting orientation features such as direction vectors or orthogonal matrices (among other examples). We first develop a general variational framework that solves this regularization problem, thanks to a constrained minimization of f-functionals. This leads to a set of coupled vector-valued PDE’s preserving the orthonormal constraints. Then, we focus on particular applications of this general framework, including the restoration of noisy direction fields, noisy chromaticity color images, estimated camera motions and DT-MRI (Diffusion Tensor MRI) datasets.
MSC:
68U99 | Computing methodologies and applications |
68T45 | Machine vision and scene understanding |
68U10 | Computing methodologies for image processing |