Open Access

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

  • Michael K. Ng1Email author,
  • Huanfeng Shen1, 2,
  • Edmund Y. Lam3 and
  • Liangpei Zhang2
EURASIP Journal on Advances in Signal Processing20072007:074585

DOI: 10.1155/2007/74585

Received: 13 September 2006

Accepted: 21 April 2007

Published: 27 June 2007


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.


Authors’ Affiliations

Department of Mathematics, Hong Kong Baptist University
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University
Department of Electrical and Electronic Engineering, The University of Hong Kong


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© Michael K. Ng et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.