In an optical see-through augmented reality system, virtual and real information can be viewed at different distances. This requires users to frequently change their eye focus from one distance to another, and only one piece of information is in sharp focus while the other is out of focus. Previous studies have found that due to out-of-focus virtual information, when integrating information between the distances, users suffer fatigue and miss important information. Therefore, this paper introduces a novel font, termed a SharpView font, which looks sharper and more legible than standard fonts when seen out of focus. Our method models out-of-focus blur with Zernike polynomials and coefficients, develops a focus correction algorithm based on constrained total variation optimization, and proposes a novel gradient-based algorithm to quantify the sharpness of textual information. We have evaluated the SharpView font through simulation and optically viewed camera-based measurement. When seen out of focus, our proposed font are significantly sharper than standard fonts, as assessed both visually and quantitatively through simulation (40%-44%), as well as the optics of an augmented reality display (24%-32%).

We describe a technique to generate imagery with improved sharpness for individuals having refractive vision problems. Our method can reduce their dependence on corrective eyewear. It also benefits individuals with normal vision by improving visual acuity at a distance and of small details. Our approach does not require custom hardware. Instead, the calculated images can be shown on a standard computer display, on printed paper, or superimposed on a physical scene using a projector. Our technique uses a constrained total variation method to produce a deconvolution result which, upon observation, appears sharp at the edges. We introduce a novel relative total variation term that enables controlling ringing reduction, contrast gain, and sharpness. The end result is the ability to generate sharper appearing images, even for individuals with refractive vision problems including myopia, hyperopia, presbyopia, and astigmatism. Our approach has been validated in simulation, in camera-screen experiments, and in a study with human observers.