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 ill-posed problem: in the...
Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 29(2007), 9 vom: 14. Sept., Seite 1647-54 |
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Auteur principal: | |
Autres auteurs: | |
Format: | Article |
Langue: | English |
Publié: |
2007
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Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
Sujets: | Evaluation Study Journal Article |
Résumé: | 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 |
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Description: | Date Completed 31.12.2007 Date Revised 09.04.2022 published: Print Citation Status MEDLINE |
ISSN: | 1939-3539 |