A total variation regularization based super-resolution reconstruction algorithm for digital video

Kwok Po NG*, Huanfeng Shen, Edmund Y. Lam, Liangpei Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

181 Citations (Scopus)

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.£.

Original languageEnglish
Article number74585
JournalEurasip Journal on Advances in Signal Processing
Volume2007
DOIs
Publication statusPublished - 2007

Scopus Subject Areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Electrical and Electronic Engineering

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