Separation of Reflection Components Using Color and Polarization
International Journal of Computer Vision
A fast fixed-point algorithm for independent component analysis
Neural Computation
Image Source Separation Using Color Channel Dependencies
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Markov model for blind image separation by a mean-field EM algorithm
IEEE Transactions on Image Processing
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We address the problem of permutation ambiguity in blind separation of multiple mixtures of multiple images (resulting, for instance, from multiple reflections through a thick grass plate or through two overlapping glass plates) with unknown mixing coefficients. In this paper, first we devise a generalized multiple correlation measure between one gray image and a set of multiple gray images and derive a decorrelation-based blind image separation algorithm. However, many blind image separation methods, including this algorithm, suffer from a permutation ambiguity problem that the success of the separation depends upon the selection of permutations corresponding to the orders of the update operations. To solve the problem, we improve the first algorithm above by decorrelating the mixtures while searching for the appropriate update permutation using a pruning technique. We show its effectiveness through experiments with artificially mixed images and real images.