1,883 research outputs found
Tomographic Image Reconstruction of Fan-Beam Projections with Equidistant Detectors using Partially Connected Neural Networks
We present a neural network approach for tomographic imaging problem using interpolation methods and fan-beam projections. This approach uses a partially connected neural network especially assembled for solving tomographic\ud
reconstruction with no need of training. We extended the calculations to perform reconstruction with interpolation and to allow tomography of fan-beam geometry. The main goal is to aggregate speed while maintaining or improving the quality of the tomographic reconstruction process
Geometric reconstruction methods for electron tomography
Electron tomography is becoming an increasingly important tool in materials
science for studying the three-dimensional morphologies and chemical
compositions of nanostructures. The image quality obtained by many current
algorithms is seriously affected by the problems of missing wedge artefacts and
nonlinear projection intensities due to diffraction effects. The former refers
to the fact that data cannot be acquired over the full tilt range;
the latter implies that for some orientations, crystalline structures can show
strong contrast changes. To overcome these problems we introduce and discuss
several algorithms from the mathematical fields of geometric and discrete
tomography. The algorithms incorporate geometric prior knowledge (mainly
convexity and homogeneity), which also in principle considerably reduces the
number of tilt angles required. Results are discussed for the reconstruction of
an InAs nanowire
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