Image Source Separation Using Color Channel Dependencies
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Bayesian separation of images modeled with MRFs using MCMC
IEEE Transactions on Image Processing
User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.