Optimal preconditioning for image deblurring with Anti-Reflective boundary conditions

TL;DR

This paper extends the theory of optimal preconditioning from Reflective to Anti-Reflective boundary conditions in image deblurring, demonstrating that the same preconditioner based on the symmetrized PSF is effective.

Contribution

It provides the first theoretical analysis of optimal preconditioning for Anti-Reflective boundary conditions, overcoming technical challenges due to non-orthogonality.

Findings

Preconditioning accelerates deblurring for symmetric and non-symmetric PSFs.

Optimal preconditioner is the blurring matrix of the symmetrized PSF.

Method is effective for both slightly and highly non-symmetric PSFs.

Abstract

Inspired by the theoretical results on optimal preconditioning stated by Ng, R.Chan, and Tang in the framework of Reflective boundary conditions (BCs), in this paper we present analogous results for Anti-Reflective BCs, where an additional technical difficulty is represented by the non orthogonal character of the Anti-Reflective transform and indeed the technique of Ng, R.Chan, and Tang can not be used. Nevertheless, in both cases, the optimal preconditioner is the blurring matrix associated to the symmetrized Point Spread Function (PSF). The geometrical idea on which our proof is based is very simple and general, so it may be useful in the future to prove theoretical results for new proposed boundary conditions. Computational results show that the preconditioning strategy is effective and it is able to give rise to a meaningful acceleration both for slightly and highly non-symmetric PSFs.