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.