A Bayesian approach to shape from coded aperture

In this paper we present analysis and a novel algorithm to estimate depth from a single image captured by a coded aperture camera. This is a challenging problem which requires new tools and investigations, compared with multi-view reconstruction. Unlike previous approaches, which need to recover both sharp image and depth, we consider directly estimating only depth, whilst still accounting for the statistics of the sharp image. The problem is formulated in a Bayesian framework, which enables us to reduce the estimation of the original sharp image to the local space-varying statistics of the texture. This yields an algorithm that can be solved via graph cuts (without user interaction). Performance and results on both synthetic and real data are reported and compared with previous methods.