Separating reflections from a single image using spatial smoothness and structure information
Yan Q., Kuruoglu E. E., Yang X., Xu Y., Kayabol K.
68U10 Image processing Restoration 62M40 Random fields Image Processing and Computer Vision. Applications 62F15 Bayesian inference image analysis
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.
Source: LVA/ICA 2010 - Latent Variable Analysis and Signal Separation. 9th International Conference, pp. 637–644, St. Malo, France, 27-30 September 2010
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@inproceedings{oai:it.cnr:prodotti:44354,
title = {Separating reflections from a single image using spatial smoothness and structure information},
author = {Yan Q. and Kuruoglu E. E. and Yang X. and Xu Y. and Kayabol K.},
doi = {10.1007/978-3-642-15995-4_79},
booktitle = {LVA/ICA 2010 - Latent Variable Analysis and Signal Separation. 9th International Conference, pp. 637–644, St. Malo, France, 27-30 September 2010},
year = {2010}
}
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