Greedy Bayesian double sparsity dictionary learning

Juan G. Serra, Salvador Villena, Rafael Molina, Aggelos K. Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This work presents a greedy Bayesian dictionary learning (DL) algorithm where not only the signals but also the dictionary representation matrix accept a sparse representation. This double-sparsity (DS) model has been shown to be superior to the standard sparse approach in some image processing tasks, where sparsity is only imposed on the signal coefficients. We present a new Bayesian approach which addresses typical shortcomings of regularization-based DS algorithms: the prior knowledge of the true noise level and the need of parameter tuning. Our model estimates the noise and sparsity levels as well as the model parameters from the observations and frequently outperforms state-of-the-art dictionary based techniques by taking into account the uncertainty of the estimates. Additionally, we introduce a versatile notation which generalizes denoising, inpainting and compressive sensing problem formulations. Finally, theoretical results are validated with denoising experiments on a set of images.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781509021758
StatePublished - Feb 20 2018
Event24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duration: Sep 17 2017Sep 20 2017

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Other24th IEEE International Conference on Image Processing, ICIP 2017


  • Bayesian Inference
  • Dictionary Learning
  • Sparse Representation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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