GPU-based processing of Hartmann–Shack images for accurate and high-speed ocular wavefront sensing

Abstract

Hartmann–Shack aberrometry is a widely used technique in the field of visual optics but, high-speed and accurate processing of Hartmann–Shack images can be a computationally expensive/resource intensive task. While some advancements have been made in achieving high-performance processing units, they have not been specifically designed for processing Hartmann–Shack images of the human eye with Graphics Processing Units. In this work, we present the first full-Graphics Processing Unit implementation of a Hartmann–Shacksensor algorithm aimed at accurately measuring ocular aberrations at a high speed from high-resolution spot pattern images. The proposed algorithm, called PaPyCS (Parallel Pyramidal Centroid Search), is inherently parallel and performs a very robust centroid search to avoid image noise and other artifacts. This is a field where the use of Graphics Processing Units have not been exploited despite the fact that they can boost Adaptive Optics systems and related closed-loop approaches. Our proposed implementation achieves processing speeds of 380 frames per second for high resolution (1280x1280 pixels) images, in addition to showing a high resilience to system and image artifacts that appear in Hartmann–Shack images from human eyes: more than 98% of the Hartmann–Shack images, with aberrations of up to 4 μ m Root Mean Square for a 5.12mm pupil diameter, were measured with less than 0.05 μ m Root Mean Square Error, which is basically negligible for ocular aberrations.