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Super Resolution of Multispectral Images using ℓ1 Image Models and Interband Correlations

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Abstract

In this paper we propose a novel super-resolution based algorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, it imposes smoothness within each band by means of the energy associated with the ℓ1 norm of vertical and horizontal first order differences of image pixel values and also takes into account the correlation among the bands of the multispectral image. The observation process is modeled using the sensor characteristics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pansharpening methods, and the quality of the results assessed both qualitatively and quantitatively.

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Authors and Affiliations

  1. Dept. de Lenguajes y Sistemas Informáticos, Universidad de Granada, 18071, Granada, Spain

    Miguel Vega

  2. Dept. de Ciencias de la Computación e I. A., Universidad de Granada, 18071, Granada, Spain

    Javier Mateos & Rafael Molina

  3. Dept. of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL, 60208-3118, USA

    Aggelos K. Katsaggelos

Authors
  1. Miguel Vega
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  2. Javier Mateos
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  3. Rafael Molina
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  4. Aggelos K. Katsaggelos
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Corresponding author

Correspondence to Javier Mateos.

Additional information

This work has been supported by the “Comisión Nacional de Ciencia y Tecnología” under contract TIN2007-65533 and the Consejería de Innovación, Ciencia y Empresa of the Junta de Andalucía under contracts P07-TIC-02698 and P07-FQM-02701.

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Vega, M., Mateos, J., Molina, R. et al. Super Resolution of Multispectral Images using ℓ1 Image Models and Interband Correlations. J Sign Process Syst 65, 509–523 (2011). https://doi.org/10.1007/s11265-010-0554-x

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  • DOI: https://doi.org/10.1007/s11265-010-0554-x

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