Incomplete angle reconstruction algorithm with the sparse optimization and the image optimal criterions
| Autor: | Shengxi Jiao, Lu Wen, Haitao Guo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | International Journal of Advanced Robotic Systems, Vol 17 (2020) |
| Druh dokumentu: | article |
| ISSN: | 1729-8814 17298814 |
| DOI: | 10.1177/1729881420916974 |
| Popis: | To solve the problem of artifact and image degradation caused by incomplete angle projection, this article presents an incomplete angle reconstruction algorithm based on sparse optimization and image optimization criterion (SO-IOC). Firstly, the joint objective function model is established based on the projection sparsity and the natural features of images. Secondly, by means of the idea of alternating direction method of multipliers, the augmented Lagrange method is used to decompose the reconstruction model into simple subproblems and the modified genetic algorithm is used for solving those subproblems. Finally, a multiobjective optimization operation is carried out to coordinate and select the candidate solutions to improve the quality of the reconstructed images. The algebraic reconstruction technique algorithm and the Split Bregman algorithm are compared with the SO-IOC algorithm. In the compared process, the mean relative error and the peak signal-to-noise ratio are used. The experimental results show the SO-IOC algorithm is best among the above three algorithms. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|