Image Reconstruction with Analytical Point Spread Functions

TL;DR

This paper introduces an analytical approach to model the point spread function using Zernike functions, enabling faster image reconstruction in atmospheric turbulence and optical aberration correction.

Contribution

It generalizes extended Zernike-Nijboer theory for analytical integration, creating a basis for rapid phase-diversity image reconstruction.

Findings

Developed a linear expansion of the PSF incorporating defocusing.

Demonstrated a fast phase-diversity reconstruction method.

Potential to accelerate existing blind deconvolution techniques.

Abstract

The image degradation produced by atmospheric turbulence and optical aberrations is usually alleviated using post-facto image reconstruction techniques, even when observing with adaptive optics systems. These techniques rely on the development of the wavefront using Zernike functions and the non-linear optimization of a certain metric. The resulting optimization procedure is computationally heavy. Our aim is to alleviate this computationally burden. To this aim, we generalize the recently developed extended Zernike-Nijboer theory to carry out the analytical integration of the Fresnel integral and present a natural basis set for the development of the point spread function in case the wavefront is described using Zernike functions. We present a linear expansion of the point spread function in terms of analytic functions which, additionally, takes defocusing into account in a natural way. This expansion is used to develop a very fast phase-diversity reconstruction technique which is demonstrated through some applications. This suggest that the linear expansion of the point spread function can be applied to accelerate other reconstruction techniques in use presently and based on blind deconvolution.