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Separating Reflections from a Single Image Using Spatial Smoothness and Structure Information

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 6365)

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.

Keywords

  • Independent Component Analysis
  • Optical Model
  • Markov Random Field
  • Independent Component Analysis
  • Color Channel

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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

  • Print ISBN: 978-3-642-15994-7

  • Online ISBN: 978-3-642-15995-4

  • eBook Packages: Computer ScienceComputer Science (R0)