Abstract

Camera lenses are a critical component of optical imaging systems, and lens imperfections compromise image quality. While tra- ditionally, sophisticated lens design and quality control aim at limiting optical aberrations, recent works [1,2,3] promote the correction of opti- cal flaws by computational means. These approaches rely on elaborate measurement procedures to characterize an optical system, and perform image correction by non-blind deconvolution. In this paper, we present a method that utilizes physically plausible assumptions to estimate non-stationary lens aberrations blind ly, and thus can correct images without knowledge of specifics of camera and lens. The blur estimation features a novel preconditioning step that enables fast deconvolution. We obtain results that are competitive with state-of-the- art non-blind approaches.