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

Michael K. Ng; Huanfeng Shen; Edmund Y. Lam; Liangpei Zhang

doi:10.1155/2007/74585

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

Michael K. Ng^*, Huanfeng Shen, Edmund Y. Lam, Liangpei Zhang

^*Corresponding author for this work

Department of Mathematics

Research output: Contribution to journal › Journal article › peer-review

212 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 language	English
Article number	74585
Journal	Eurasip Journal on Advances in Signal Processing
Volume	2007
DOIs	https://doi.org/10.1155/2007/74585
Publication status	Published - 2007

Scopus Subject Areas

Signal Processing
Information Systems
Hardware and Architecture
Electrical and Electronic Engineering

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10.1155/2007/74585

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@article{8162072d79744a05b13cf5908846f0e3,

title = "A total variation regularization based super-resolution reconstruction algorithm for digital video",

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.£.",

author = "Ng, {Michael K.} and Huanfeng Shen and Lam, {Edmund Y.} and Liangpei Zhang",

note = "Research supported in part by RGC 7035/04P and 7035/05P, and HKBU FRGs. H. Shen would like to thank M. K. Ng for his hospitality during his visit to Centre for Mathematical Imaging and Vision, Hong Kong Baptist University, from March 2006 to March 2007. This work was done during H. Shen visit to Hong Kong Baptist University. Research supported in part by RGC Grant HKU 7143/05E. ",

year = "2007",

doi = "10.1155/2007/74585",

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volume = "2007",

journal = "Eurasip Journal on Advances in Signal Processing",

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T1 - A total variation regularization based super-resolution reconstruction algorithm for digital video

AU - Ng, Michael K.

AU - Shen, Huanfeng

AU - Lam, Edmund Y.

AU - Zhang, Liangpei

N1 - Research supported in part by RGC 7035/04P and 7035/05P, and HKBU FRGs. H. Shen would like to thank M. K. Ng for his hospitality during his visit to Centre for Mathematical Imaging and Vision, Hong Kong Baptist University, from March 2006 to March 2007. This work was done during H. Shen visit to Hong Kong Baptist University. Research supported in part by RGC Grant HKU 7143/05E.

PY - 2007

Y1 - 2007

N2 - 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.£.

AB - 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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DO - 10.1155/2007/74585

M3 - Journal article

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VL - 2007

JO - Eurasip Journal on Advances in Signal Processing

JF - Eurasip Journal on Advances in Signal Processing

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

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