{"id":159418,"date":"2010-06-01T00:00:00","date_gmt":"2010-06-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/a-spatially-varying-psf-based-prior-for-alpha-matting\/"},"modified":"2018-10-16T21:58:46","modified_gmt":"2018-10-17T04:58:46","slug":"a-spatially-varying-psf-based-prior-for-alpha-matting","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/a-spatially-varying-psf-based-prior-for-alpha-matting\/","title":{"rendered":"A Spatially Varying PSF-based Prior for Alpha Matting"},"content":{"rendered":"<p>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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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. 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