A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video. (27th June 2007)

• Record Type: Journal Article
• Authors: Ng Ng, Michael K. Michael K.; Shen Shen, Huanfeng Huanfeng; Lam Lam, Edmund Y. Edmund Y.; Zhang Zhang, Liangpei Liangpei
• Other Names: Hardie Hardie Russell C. Russell C. Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2007-06-27
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/74585
• Languages: English
• ISSNs: 1687-6172

Abstract

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.