Abstract
We adopt two priors to realize reflection separation from a single image, namely spatial smoothness, which is based on pixels’ color dependency, and structure difference, which is got from different source images (transmitted image and reflected image) and different color channels of the same image. By analysing the optical model of reflection, we simplify the mixing matrix further and realize the method for getting spatially varying mixing coefficients. Based on the priors and using Gibbs sampling and appropriate probability density with Bayesian framework, our approach can achieve impressive results for many real world images that corrupted with reflections.
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References
Born, M., Wolf, E.: Principles of Optics. Pergamon, London (1965)
Farid, H., Adelson, E.H.: Separating reflections and lighting using independent components analysis. CVPR 1, 262–267 (1999)
Bronstein, A.M., Bronstein, M.M., Zibulevsky, M., Zeevi, Y.Y.: Sparse ICA for blind separation of transmitted and reflected images. International Journal of Imaging System and Technology 15(1), 84–91 (2005)
Be’ery, E., Yeredor, A.: Blind separation of reflections with relative spatial shifts. In: ICASSP, vol. 5, pp. 625–628 (2006)
Gai, K., Shi, Z.W., Zhang, C.S.: Blindly separating mixtures of multiple layers with spatial shifts. In: CVPR, pp. 1–8 (2008)
Gai, K., Shi, Z.W., Zhang, C.S.: Blind separation of superimposed images with unknown motions. In: CVPR, pp. 1881–1888 (2009)
Sarel, B., Irani, M.: Separating transparent layers through layer information exchange. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 328–341. Springer, Heidelberg (2004)
Levin, A., Zomet, A., Weiss, Y.: Separating reflections from a single image using local features. In: CVPR, pp. 306–313 (2004)
Levin, A., Weiss, Y.: User assisted separation of reflections from a single image using a sparsity prior. IEEE Trans. Pattern Analysis and Machine Intelligence 29(9), 1647–1655 (2007)
Kayabol, K., Kuruoglu, E.E., Sankur, B.: Image source separation using color channel dependencies. In: Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation, pp. 499–506 (2009)
Kayabol, K., Kuruoglu, E.E., Sankur, B.: Bayesian separation of images modeled with MRFs using MCMC. IEEE Trans. Image Process. 18(5), 982–994 (2009)
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Yan, Q., Kuruoglu, E.E., Yang, X., Xu, Y., Kayabol, K. (2010). Separating Reflections from a Single Image Using Spatial Smoothness and Structure Information. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_79
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DOI: https://doi.org/10.1007/978-3-642-15995-4_79
Publisher Name: Springer, Berlin, Heidelberg
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