Space-variant Shack-Hartmann wavefront sensing based on affine transformation estimation

TL;DR

This paper introduces a novel space-variant wavefront sensing framework using affine transformation estimation, significantly improving reconstruction accuracy in deep tissue imaging by modeling wavefronts as four-dimensional functions.

Contribution

It proposes a new wavefront sensing method that models space-variant wavefronts as four-dimensional functions and uses affine transformations for improved reconstruction accuracy.

Findings

Achieved 2-4 times better accuracy than traditional methods.

Extended zonal and modal methods to four-dimensional wavefront representation.

Validated effectiveness through experiments and simulations.

Abstract

The space-variant wavefront reconstruction problem inherently exists in deep tissue imaging. In this paper,we propose a framework of Shack-Hartmann wavefront space-variant sensing with extended source illumination. The space-variant wavefront is modeled as a four-dimensional function where two dimensionsare in the spatial domain and two in the Fourier domain with priors that both gently vary. Here, the affinetransformation is used to characterize the wavefront space-variant function. Correspondingly, the zonaland modal methods are both escalated to adapt to four-dimensional representation and reconstruction.Experiments and simulations show double to quadruple improvements in space-variant wavefront reconstruction accuracy compared to the conventional space-invariant correlation method.