Modeling and Estimation of Spatially-Varying Point-Spread Functions due to Lens Aberrations and Defocus

Many image restoration and analysis approaches in the literature rely on an accurate characterization of the linear blur kernel for an image, the point-spread function (PSF). Existing PSF models are either parameterized and spatially-invariant, or spatially-varying and discretely-defined. In this thesis, we propose a parameterized, spatially-varying PSF model to describe the blur due to lens aberrations and defocus. The model follows from the combination of several geometric camera models, and the Seidel third-order aberration model. We propose a novel estimation algorithm for computing the parameters of the aberration model from a set of PSF observations, and we demonstrate through simulation that this yields a more reliable set of PSF estimates. In simulated PSF sets with spread measure noise as strong as 10 dB SNR, the proposed model consistently led to PSF estimates with a 5 dB SNR improvement over the observations, and typically a 10 dB SNR improvement.