Blind Image Separation Using Nonnegative Matrix Factorization with Gibbs Smoothing

Nonnegative Matrix Factorization (NMF) has already found many applications in image processing and data analysis, including classification, clustering, feature extraction, pattern recognition, and blind image separation. In the paper, we extend the selected NMF algorithms by taking into account local smoothness properties of source images. Our modifications are related with incorporation of the Gibbs prior, which is well-known in many tomographic image reconstruction applications, to a underlying blind image separation model. The numerical results demonstrate the improved performance of the proposed methods in comparison to the standard NMF algorithms.

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