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Adaptive vectorial lifting concept for convolutive blind source separation

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Abstract

This paper describes a new multi-resolution approach for the blind separation of convolutive image mixtures in transform domain. The proposed method uses an Adaptive Vectorial case of Quincunx Lifting Scheme (AVQLS), based on wavelet decomposition, and a geometric unmixing algorithm. It proceeds in three steps: first, the mixed images are decomposed by AVQLS. Then, the unmixing algorithm is applied to the more relevant component to get a transformed estimate of the original images. An inverse transform is, thereafter, applied to obtain an estimate of the original images. Experiments carried out on medical images showed that the proposed method yields better separation results than many widely used blind source separation algorithms.

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Authors and Affiliations

  1. Communication System Laboratory Sys’Com, National Engineering School Of Tunis, University Tunis El Manar, El Manar, Tunisia

    Jamel Hattay & Taoufik Aguili

  2. University of Monastir, Monastir, Tunisia

    Samir Belaid & Wady Naanaa

Authors
  1. Jamel Hattay
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  2. Samir Belaid
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  3. Wady Naanaa
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  4. Taoufik Aguili
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Correspondence to Jamel Hattay.

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Hattay, J., Belaid, S., Naanaa, W. et al. Adaptive vectorial lifting concept for convolutive blind source separation. Pattern Anal Applic 20, 507–518 (2017). https://doi.org/10.1007/s10044-015-0517-8

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