Image reconstruction algorithm based on variable atomic number matching pursuit. Issue 2 (June 2017)

• Record Type: Journal Article
• Authors: Zhao, Hongtu; Chen, Chong; Shi, Chenxu
• Is Part Of: Journal of algorithms & computational technology. Volume 11:Issue 2(2017)
• Page Start: 103
• Page End: 109
• Publication Date: 2017-06
• Subjects: Image reconstruction -- compressive sensing -- convex optimization algorithm -- greedy algorithm -- variable atomic number matching pursuit; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748301816673074
• Languages: English
• ISSNs: 1748-3018

Abstract

As the most critical part of compressive sensing theory, reconstruction algorithm has an impact on the quality and speed of image reconstruction. After studying some existing convex optimization algorithms and greedy algorithms, we find that convex optimization algorithms should possess higher complexity to achieve higher reconstruction quality. Also, fixed atomic numbers used in most greedy algorithms increase the complexity of reconstruction. In this context, we propose a novel algorithm, called variable atomic number matching pursuit, which can improve the accuracy and speed of reconstruction. Simulation results show that variable atomic number matching pursuit is a fast and stable reconstruction algorithm and better than the other reconstruction algorithms under the same conditions.