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.