Peak-locking centroid bias in Shack-Hartmann wavefront sensing

TL;DR

This paper systematically studies the bias errors in centroid measurements for Shack-Hartmann wavefront sensors, compares various algorithms, and proposes a practical method to significantly reduce bias across different imaging conditions.

Contribution

It provides the first comprehensive quantification of centroid bias errors and introduces a practical bias mitigation technique applicable to diverse sub-aperture images.

Findings

Bias errors vary with image type and algorithm.

The proposed method reduces bias by a factor of ~7.

Bias can be minimized to below 0.02 pixels.

Abstract

Shack-Hartmann wavefront sensing relies on accurate spot centre measurement. Several algorithms were developed with this aim, mostly focused on precision, i.e. minimizing random errors. In the solar and extended scene community, the importance of the accuracy (bias error due to peak-locking, quantisation or sampling) of the centroid determination was identified and solutions proposed. But these solutions only allow partial bias corrections. To date, no systematic study of the bias error was conducted. This article bridges the gap by quantifying the bias error for different correlation peak-finding algorithms and types of sub-aperture images and by proposing a practical solution to minimize its effects. Four classes of sub-aperture images (point source, elongated laser guide star, crowded field and solar extended scene) together with five types of peak-finding algorithms (1D parabola, the centre of gravity, Gaussian, 2D quadratic polynomial and pyramid) are considered, in a variety of signal-to-noise conditions. The best performing peak-finding algorithm depends on the sub-aperture image type, but none is satisfactory to both bias and random errors. A practical solution is proposed that relies on the anti-symmetric response of the bias to the sub-pixel position of the true centre. The solution decreases the bias by a factor of ~7 to values of < 0.02 pix. The computational cost is typically twice of current cross-correlation algorithms.