title: User assisted separation of reflections from a single image using a sparsity prior.
abstract: 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 ill-posed 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. This sparsity prior is optimized using the terative reweighted least squares (IRLS) approach. Our results show that using a prior derived from the statistics of natural images gives a far superior performance compared to a Gaussian prior and it enables good separations from a modest number of labeled gradients.
authors.affiliation: School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel. alevin@cs.huji.ac.il
authors.forename: Anat
authors.initials: A
authors.lastname: Levin
authors.forename: Yair
authors.initials: Y
authors.lastname: Weiss
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012031:Refractometry / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity