Abstract

Computer vision has traditionally focused on extracting structure, such as depth, from images acquired using thin-lens or pinhole optics. The de- velopment of computational imaging is broadening this scope; a variety of un- conventional cameras do not directly capture a traditional image anymore, but instead require the joint reconstruction of structure and image information. For example, recent coded aperture designs have been optimized to facilitate the joint reconstruction of depth and intensity. The breadth of imaging designs requires new tools to understand the tradeoffs implied by different strategies. This paper introduces a uni fied framework for analyzing computational imag- ing approaches. Each sensor element is modeled as an inner product over the 4D light field. The imaging task is then posed as Bayesian inference: given the observed noisy light field projections and a prior on light field signals, estimate the original light field. Under common imaging conditions, we compare the per- formance of various camera designs using 2D light field simulations. This frame- work allows us to better understand the tradeoffs of each camera type and analyze their limitations.