Mapping measurable quantities of point-spread function observations to Seidel aberration coefficients
The Seidel aberration model has proven an invaluable tool in the design of optical systems [1, 2], by providing a model that bridges the gap between first-order optics models and actual system performance. However, it has been largely neglected in the modeling of blur kernels, despite the ability of the model to accurately predict the point-spread function (PSF) of an optical system due to aberration and defocus. As a step towards developing a parameterized, spatially-varying PSF model, we propose a novel mapping to Seidel aberration coefficients from observable spread measures of discretely-defined PSFs. We demonstrate through simulation that this mapping, and the associated estimation algorithm, allow a noisy dataset of 120 PSF observations (consisting of thousands of degrees of freedom) to be unified under a single 4-parameter model.