A Spatially Varying PSF-Based Prior for Alpha Matting

Abstract

In this paper we considerably improve on a state-of-the-art alpha matting approach by incorporating a new prior which is based on the image formation process. In par-ticular, we model the prior probability of an alpha matte as the convolution of a high-resolution binary segmentation with the spatially varying point spread function (PSF) of the camera. Our main contribution is a new and efficient de-convolution approach that recovers the prior model, given an approximate alpha matte. By assuming that the PSF is a kernel with a single peak, we are able to recover the bi-nary segmentation with an MRF-based approach, which ex-ploits flux and a new way of enforcing connectivity. The spatially varying PSF is obtained via a partitioning of the image into regions of similar defocus. Incorporating our new prior model into a state-of-the-art matting technique produces results that outperform all competitors, which we confirm using a publicly available benchmark. 1.