METRIC GRAPH RECONSTRUCTION FROM NOISY DATA

Tomographic Reconstruction from Noisy Data

A generalized maximum entropy based approach to noisy inverse problems such as the Abel problem, tomography, or deconvolution is discussed and reviewed. Unlike the more traditional regularization approach, in the method discussed here, each unknown parameter (signal and noise) is redefined as a proper probability distribution within a certain pre-specified support. Then, the joint entropies of ...

Anatomically constrained reconstruction from noisy data.

Noise is a major concern in many important imaging applications. To improve data signal-to-noise ratio (SNR), experiments often focus on collecting low-frequency k-space data. This article proposes a new scheme to enable extended k-space sampling in these contexts. It is shown that the degradation in SNR associated with extended sampling can be effectively mitigated by using statistical modelin...

Jump-Preserving Surface Reconstruction From Noisy Data

A new local smoothing procedure is suggested for jump-preserving surface reconstruction from noisy data. In a neighborhood of a given point in the design space, a plane is fitted by local linear kernel smoothing, giving the conventional local linear kernel estimator of the surface at the point. The neighborhood is then divided into two parts by a line passing through the given point and perpend...

Robust Surface Reconstruction from Noisy Point Clouds using Graph Cuts

This work is concerned with the implementation and evaluation of a surface reconstruction approach, which is highly robust against outliers. The chosen approach has a range of appealing properties. First of all, it manages to incorporate a keypoint-based visibility constraint, which leads to a very high resilience to outliers. Secondly, it is based on a Delaunay triangulation, which makes the a...

Image Reconstruction from Incomplete Data