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A Computationally Efficient Superresolution Image Reconstruction Algorithm (2000)
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BibTeX
@MISC{Nguyen00acomputationally,
author = {Nhat Nguyen and Peyman Milanfar and Gene Golub},
title = {A Computationally Efficient Superresolution Image Reconstruction Algorithm},
year = {2000}
}
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Abstract
Superresolution reconstruction produces a high-resolution image from a set of low-resolution images. Previous iterative methods for superresolution had not adequately addressed the computational and numerical issues for this ill-conditioned and typically underdetermined large scale problem. We propose efficient block circulant preconditioners for solving the Tikhonov-regularized superresolution problem by the conjugate gradient method. We also extend to underdetermined systems the derivation of the generalized cross-validation method for automatic calculation of regularization parameters. Effectiveness of our preconditioners and regularization techniques is demonstrated with superresolution results for a simulated sequence and a forward looking infrared (FLIR) camera image sequence.
Keyphrases
high-resolution image camera image sequence simulated sequence efficient block circulant preconditioners generalized cross-validation method conjugate gradient method superresolution result previous iterative method regularization parameter tikhonov-regularized superresolution problem underdetermined system numerical issue low-resolution image superresolution reconstruction underdetermined large scale problem regularization technique automatic calculation
