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Constrained Total Least Squares for Color Image Reconstruction

In: Total Least Squares and Errors-in-Variables Modeling

Author

Listed:
  • Michael K. Ng

    (The University of Hong Kong, Department of Mathematics)

  • Nirmal K. Bose

    (The Pennsylvania State University, Department of Electrical Engineering Signal Processing Center)

  • Jaehoon Koo

    (The Pennsylvania State University, Department of Electrical Engineering Signal Processing Center)

Abstract

Multiple undersampled color images of a scene are often obtained by using a charge-coupled device detector array of sensors which are shifted relative to each other by subpixel displacements. This geometry of sensors, where each color sensor has a subarray of sensing elements of suitable size has recently been popular in the task of attaining spatial resolution enhancement from the acquired low-resolution degraded color images that comprise the set of observations. With the objective of improving the performance of the signal-processing algorithms in the presence of the ubiquitous perturbation errors of displacements around the ideal subpixel locations (because of imperfections in fabrication) in addition to noisy observations, the regularized constrained total least squares (RCTLS) method is deployed here. The expected superiority of this RCTLS approach over the conventional least squares theory based algorithm is demonstrated by example.

Suggested Citation

  • Michael K. Ng & Nirmal K. Bose & Jaehoon Koo, 2002. "Constrained Total Least Squares for Color Image Reconstruction," Springer Books, in: Sabine Van Huffel & Philippe Lemmerling (ed.), Total Least Squares and Errors-in-Variables Modeling, pages 365-374, Springer.
    • Handle: RePEc:spr:sprchp:978-94-017-3552-0_32
    DOI: 10.1007/978-94-017-3552-0_32
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