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A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video (2007)
| Citations: | 10 - 0 self |
BibTeX
@MISC{Ng07atotal,
author = {Michael K. Ng and Huanfeng Shen and Edmund Y. Lam and Liangpei Zhang},
title = { A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video},
year = {2007}
}
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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.
Keyphrases
digital video research article total variation regularization super-resolution reconstruction algorithm compression artifact associated nonlinear euler-lagrange equation original work reconstruction technique fixed-point iteration method inevitable motion estimation error creative common attribution license low-resolution image intractable problem sr video reconstruction total variation unrestricted use reconstruction model tv-based sr algorithm corresponding variational problem high-resolution image laplacian regularization-based sr algorithm efficient sr algorithm open access article experimental result several case
