@Article{cmes.2022.020656,
AUTHOR = {Luyao Yang, Hao Chen, Haocheng Yu, Jin Qiu, Shuxian Zhu},
TITLE = {A Fixed-Point Iterative Method for Discrete Tomography Reconstruction Based on Intelligent Optimization},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {1},
PAGES = {731--745},
URL = {http://www.techscience.com/CMES/v134n1/49447},
ISSN = {1526-1506},
ABSTRACT = {Discrete Tomography (DT) is a technology that uses image projection to reconstruct images. Its reconstruction
problem, especially the binary image (0–1 matrix) has attracted strong attention. In this study, a fixed point iterative
method of integer programming based on intelligent optimization is proposed to optimize the reconstructed model.
The solution process can be divided into two procedures. First, the DT problem is reformulated into a polyhedron
judgment problem based on lattice basis reduction. Second, the fixed-point iterative method of Dang and Ye is used
to judge whether an integer point exists in the polyhedron of the previous program. All the programs involved in
this study are written in MATLAB. The final experimental data show that this method is obviously better than
the branch and bound method in terms of computational efficiency, especially in the case of high dimension. The
branch and bound method requires more branch operations and takes a long time. It also needs to store a large
number of leaf node boundaries and the corresponding consumption matrix, which occupies a large memory space.},
DOI = {10.32604/cmes.2022.020656}
}