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Blind superresolution

In: Compstat 2006 - Proceedings in Computational Statistics

Author

Listed:
  • Filip Šroubek

    (Academy of Sciences of the Czech Republic, ÚTIA
    CSIC, Instituto de Óptica)

  • Gabriel Cristóbal

    (CSIC, Instituto de Óptica)

  • Jan Flusser

    (Academy of Sciences of the Czech Republic, ÚTIA)

Abstract

This paper presents a unifying approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene. We do not assume any prior information about the shape of degradation blurs. The proposed approach consists of building a regularized energy function and minimizing it with respect to the original image and blurs, where regularization is carried out in both the image and blur domains. The image regularization based on variational principles maintains stable performance under severe noise corruption. The blur regularization guarantees consistency of the solution by exploiting differences among the acquired low-resolution images. Experiments on real data illustrate the robustness and utilization of the proposed technique.

Suggested Citation

  • Filip Šroubek & Gabriel Cristóbal & Jan Flusser, 2006. "Blind superresolution," Springer Books, in: Alfredo Rizzi & Maurizio Vichi (ed.), Compstat 2006 - Proceedings in Computational Statistics, pages 133-145, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-1709-6_11
    DOI: 10.1007/978-3-7908-1709-6_11
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