Improving reconstructions in nanotomography for homogeneous materials via mathematical optimization
arxiv(2023)
摘要
Compressed sensing is an image reconstruction technique to achieve
high-quality results from limited amount of data. In order to achieve this, it
utilizes prior knowledge about the samples that shall be reconstructed.
Focusing on image reconstruction in nanotomography, this work proposes
enhancements by including additional problem-specific knowledge. In more
detail, we propose further classes of algebraic inequalities that are added to
the compressed sensing model. The first consists in a valid upper bound on the
pixel brightness. It only exploits general information about the projections
and is thus applicable to a broad range of reconstruction problems. The second
class is applicable whenever the sample material is of roughly homogeneous
composition. The model favors a constant density and penalizes deviations from
it. The resulting mathematical optimization models are algorithmically
tractable and can be solved to global optimality by state-of-the-art available
implementations of interior point methods. In order to evaluate the novel
models, obtained results are compared to existing image reconstruction methods,
tested on simulated and experimental data sets. The experimental data comprise
one 360{\deg} electron tomography tilt series of a macroporous zeolite particle
and one absorption contrast nano X-ray computed tomography (nano-CT) data set
of a copper microlattice structure. The enriched models are optimized quickly
and show improved reconstruction quality, outperforming the existing models.
Promisingly, our approach yields superior reconstruction results, particularly
when information about the samples is available for a small number of tilt
angles only
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