Transfer efficiency and depth invariance in computational cameras

Recent advances in computational cameras achieve extension of depth of field by modulating the aperture of an imaging system, either spatially or temporally. They are, however, accompanied by loss of image detail, the chief cause of which is low and/or depth-varying frequency response of such systems. In this paper, we examine the tradeoff between achieving depth invariance and maintaining high transfer efficiency by providing a mathematical framework for analyzing the transfer function of these computational cameras. Using this framework, we prove mathematical bounds on the efficacy of the tradeoff. These bounds lead to observations on the fundamental limitations of computational cameras. In particular, we show that some existing designs are already near-optimal in our metrics.