A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video
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Research Article A Total Variation Regularization Based Super-Resolution Reconstruction Algorithm for Digital Video Michael K. Ng,1 Huanfeng Shen,1, 2 Edmund Y. Lam,3 and Liangpei Zhang2 1 Department
of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China 3 Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong 2 The
Received 13 September 2006; Revised 12 March 2007; Accepted 21 April 2007 Recommended by Russell C. Hardie 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. Copyright © 2007 Michael K. Ng et al. 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.
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INTRODUCTION
Solid-state sensors such as CCD or CMOS are widely used nowadays in many image acquisition systems. Such sensors consist of rectangular arrays of photodetectors where their physical sizes limit the spatial resolution of acquired images. In order to increase the spatial resolution of images, one possibility is to reduce the size of rectangular array elements by using advanced sensor fabrication techniques. However, this method would lead to a small signal-to-noise ratio (SNR) because the amount of photons collected in each photodetector decreases correspondingly. On the other hand, the cost of manufacturing such sensors increases rapidly as the number of pixels in a sensor increases. Moreover, in some applications, we only obtain low-resolution (LR) images. In order to get a more desirable high-resolution (HR) images, superresolution (SR) technique can be employed as an effective and efficient alternative. Super-resolution image reconstruction refers to a process that produces an HR image from a sequence of LR images using the nonredundant information among them. It overcomes the inherent resolution limitation by bringing together the additional information from each LR image.
Generally, SR techniques can be divided into
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