A spatially varying PSF-based prior for alpha matting

TitleA spatially varying PSF-based prior for alpha matting
Publication TypeConference Paper
Year of Publication2010
AuthorsRhemann, C, Rother, C, Kohli, P, Gelautz, M
Conference NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISBN Number9781424469840

In this paper we considerably improve on a state-of-theart alpha matting approach by incorporating a new prior which is based on the image formation process. In particular, 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 deconvolution 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 binary segmentation with an MRF-based approach, which exploits 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. ©2010 IEEE.

Citation KeyRhemann2010