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A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video

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Abstract

Super-resolution (SR) reconstruction technique is capable of producing a high-resolution image from a sequence of low-resolution images. In this paper, we study an efficient SR algorithm for digital video. To effectively deal with the intractable problems in SR video reconstruction, such as inevitable motion estimation errors, noise, blurring, missing regions, and compression artifacts, the total variation (TV) regularization is employed in the reconstruction model. We use the fixed-point iteration method and preconditioning techniques to efficiently solve the associated nonlinear Euler-Lagrange equations of the corresponding variational problem in SR. The proposed algorithm has been tested in several cases of motion and degradation. It is also compared with the Laplacian regularization-based SR algorithm and other TV-based SR algorithms. Experimental results are presented to illustrate the effectiveness of the proposed algorithm.

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Correspondence to Michael K. Ng.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Ng, M.K., Shen, H., Lam, E.Y. et al. A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video. EURASIP J. Adv. Signal Process. 2007, 074585 (2007) doi:10.1155/2007/74585

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Keywords

  • Reconstruction Algorithm
  • Motion Estimation
  • Reconstruction Technique
  • Digital Video
  • Reconstruction Model