title: Preconditioning for multiplexed imaging with spatially coded PSFs.
abstract: We propose a preconditioning method to improve the convergence of iterative reconstruction algorithms in multiplexed imaging based on convolution-based compressive sensing with spatially coded point spread functions (PSFs). The system matrix is converted to improve the condition number with a preconditioner matrix. The preconditioner matrix is calculated by Tikhonov regularization in the frequency domain. The method was demonstrated with simulations and an experiment involving a range detection system with a grating based on the multiplexed imaging framework. The results of the demonstrations showed improved reconstruction fidelity by using the proposed preconditioning method.
authors.affiliation: Department of Information and Physical Sciences, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan. r.horisaki@ist.osaka-u.ac.jp
authors.forename: Ryoichi
authors.initials: R
authors.lastname: Horisaki
authors.forename: Jun
authors.initials: J
authors.lastname: Tanida
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D044962:Data Compression / Q000379:methods*; D008962:Models, Theoretical; D055095:Optics and Photonics / Q000379:methods*