Modeling Non-Stationary Asymmetric Lens Blur by Normal Sinh-Arcsinh Model

Abstract

Images acquired by a camera show lens blur due to imperfection in the optical system even when images are properly focused. Lens blur is non-stationary in a sense that the amount of blur depends on pixel locations in a sensor. Lens blur is also asymmetric in a sense that the amount of blur is different in the radial and tangential directions, and also in the inward and outward radial directions. This paper presents parametric blur kernel models based on the normal sinh-arcsinh distribution function. The proposed models can provide flexible shapes of blur kernels with a different symmetry and skewness to model complicated lens blur due to optical aberration in a properly focused images accurately. Blur of single focal length lenses is estimated, and the accuracy of the models is compared with the existing parametric blur models. An advantage of the proposed models is demonstrated through deblurring experiments.