@InProceedings{8902589,
  author = {M. Vega and J. Mateos and R. Molina and A. K. Katsaggelos},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Variational Bayes Color Deconvolution with a Total Variation Prior},
  year = {2019},
  pages = {1-5},
  abstract = {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.},
  keywords = {Bayes methods;image colour analysis;inference mechanisms;medical image processing;Bayesian framework;reference color-vector matrix;variational inference;digital brightfield microscopy;single stained images;blind color deconvolution problem;variational Bayes color deconvolution;multistained images;Manganese;Image color analysis;Deconvolution;Bayes methods;TV;Europe;Signal processing;Blind color deconvolution;histopathological images;variational Bayes;total variation},
  doi = {10.23919/EUSIPCO.2019.8902589},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533489.pdf},
}

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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. 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