User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior

When we take a picture through transparent glass the image we obtain is often a linear superposition of two images: the image of the scene beyond the glass plus the image of the scene reflected by the glass. Decomposing the single input image into two images is a massively illposed problem: in the absence of additional knowledge about the scene being viewed there are an infinite number of valid decompositions. In this paper we focus on an easier problem: user assisted separation in which the user interactively labels a small number of gradients as belonging to one of the layers. Even given labels on part of the gradients, the problem is still ill-posed and additional prior knowledge is needed. Following recent results on the statistics of natural images we use a sparsity prior over derivative filters. We first approximate this sparse prior with a Laplacian prior and obtain a simple, convex optimization problem. We then use the solution with the Laplacian prior as an initialization for ...

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Anat Levin, Yair Weiss
• Keywords: Additional Prior Knowledge, Computer Vision, Convex Optimization Problem, Easier Problem, ECCV 2004, Single Input Image, Small Number