Improving reconstructions in nanotomography for homogeneous materials via mathematical optimization.

ABSTRACT: 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

SUBMITTER: Kreuz S 

PROVIDER: S-EPMC11265594 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature