Variational Bayes Color Deconvolution with a Total Variation Prior

In digital brightfield microscopy, tissues are usually stained with two or more dyes. Color deconvolution aims at separating multi-stained images into single stained images. We formulate the blind color deconvolution problem within the Bayesian framework. Our model takes into account the similarity to a given reference color-vector matrix and spatial relations among the concentration pixels by a total variation prior. It utilizes variational inference and an evidence lower bound to estimate all the latent variables. The proposed algorithm is tested on real images and compared with classical and state-of-the-art color deconvolution algorithms.