Wavefront aberration statistics in normal eye populations: are they well described by the Kolmogorov model?

This Letter studies the statistics of wavefront aberrations in a sample of eyes with normal vision. Methods relying on the statistics of the measured wavefront slopes are used, not including the aberration estimation stage. Power-law aberration models, an extension of the Kolmogorov one, are rejected by χ2-tests performed on fits to the slope structure function data. This is due to the large weight of defocus and astigmatism variations in normal eyes. Models of only second-order changes are not ruled out. The results are compared with previous works in the area.

Estimating the eye aberration coefficients in resized pupils: is it better to refit or to rescale?

In order to work in a consistent way with Zernike aberration coefficients estimated in different pupils, it is necessary to refer them to a common pupil size. Two standard approaches can be used to that end: to rescale algebraically the coefficients estimated in the original pupil or to refit them anew using the wavefront slope measurements available within the new one. These procedures are not equivalent; they are affected by different estimation errors that we address in this work. Our results for normal eye populations show that in case of reducing the pupil size it is better to rescale the original coefficients than to refit them using the measurements contained within the smaller pupil. In case of enlarging the pupil size, as it can a priori be expected, the opposite holds true. We provide explicit expressions to quantify the errors arising in both cases, including the expected error incurred when extrapolating the Zernike estimation beyond the radius where the measurements were made.

Signal-to-noise ratio and aberration statistics in ocular aberrometry.

We define a signal-to-noise ratio (SNR) for eye aberrometry in terms of the sensor geometry, measurement noise, and population statistics. The overall estimation error is composed of three main contributions: the bias in the estimated modes, the truncation error, and the error due to the noise propagation. This last term can be easily parametrized by the proposed SNR. We compute the overall error as well as the magnitude of its three components for a typical sensor configuration, population statistics, and different SNR. We show that there are an optimum number of Zernike aberration modes to be retrieved in each case.

Centroid displacement statistics of the eye aberration.

We discuss a method for the study of the spatial statistics of the ocular aberrations, based on the direct use of the Hartmann-Shack centroid displacements, avoiding the wavefront reconstruction step. Centroid diagrams are introduced as a helpful aid to visualize basic properties of the aberration datasets, and slope-related second-order statistical functions are applied to check the compatibility between the experimental data and different models for the aberration statistics. Preliminary results suggest that no single power-law spectrum (e.g., Kolmogorov's) is able to represent the whole range of spatial statistics of individual eye fluctuations and that more elaborated models, including at least the contribution of a relevant defocus fluctuation term, are required. This centroid-based approach allows for an easier intercomparison of results between laboratories and avoids the bias and information loss associated with the estimation of a reduced number of Zernike coefficients from a much wider slope data set.

Experimental validation of a Bayesian model of visual acuity.

Based on standard procedures used in optometry clinics, we compare measurements of visual acuity for 10 subjects (11 eyes tested) in the presence of natural ocular aberrations and different degrees of induced defocus, with the predictions given by a Bayesian model customized with aberrometric data of the eye. The absolute predictions of the model, without any adjustment, show good agreement with the experimental data, in terms of correlation and absolute error. The efficiency of the model is discussed in comparison with image quality metrics and other customized visual process models. An analysis of the importance and customization of each stage of the model is also given; it stresses the potential high predictive power from precise modeling of ocular and neural transfer functions.