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

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2 Author(s)
Levin, A. ; Hebrew Univ. of Jerusalem, Jerusalem ; Weiss, Y.

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 iterative 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.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:29 ,  Issue: 9 )

Date of Publication:

Sept. 2007

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