When we take a picture through a window, the image we obtain is often a linear superposition of two images: the image of the scene beyond the window plus the image of the scene reflected by the window. 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 is an infinite number of valid decompositions. We describe an algorithm that uses an extremely simple form of prior knowledge to perform the decomposition. Given a single image as input, the algorithm searches for a decomposition into two images that minimize the total amount of edges and comers. The search is performed using belief propagation on a patch representation of the image. We show that this simple prior is surprisingly powerful: our algorithm obtains "correct" separations on challenging reflection scenes using only a single image.


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    Title :

    Separating reflections from a single image using local features


    Contributors:
    Levin, A. (author) / Zomet, A. (author) / Weiss, Y. (author)


    Publication date :

    2004-01-01


    Size :

    717941 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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