Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.acha.2006.09.005
Title: Superresolution image reconstruction using fast inpainting algorithms
Authors: Chan, T.F.
Ng, M.K.
Yau, A.C.
Yip, A.M. 
Issue Date: Jul-2007
Citation: Chan, T.F., Ng, M.K., Yau, A.C., Yip, A.M. (2007-07). Superresolution image reconstruction using fast inpainting algorithms. Applied and Computational Harmonic Analysis 23 (1) : 3-24. ScholarBank@NUS Repository. https://doi.org/10.1016/j.acha.2006.09.005
Abstract: The main aim of this paper is to employ the total variation (TV) inpainting model to superresolution imaging problems. We focus on the problem of reconstructing a high-resolution image from several decimated, blurred and noisy low-resolution versions of the high-resolution image. We propose a general framework for multiple shifted and multiple blurred low-resolution image frames which subsumes several well-known superresolution models. Moreover, our framework allows an arbitrary pattern of missing pixels and in particular missing frames. The proposed model combines the TV inpainting model with the framework to formulate the superresolution image reconstruction problem as an optimization problem. A distinct feature of our model is that in regions without missing pixels, the reconstruction process is regularized by TV minimization whereas in regions with missing pixels or missing frames, they are reconstructed automatically by means of TV inpainting. A fast algorithm based on fixed-point iterations and preconditioning techniques is investigated to solve the associated Euler-Lagrange equations. Experimental results are given to show that the proposed TV superresolution imaging model is effective and the proposed algorithm is efficient. © 2007 Elsevier Inc. All rights reserved.
Source Title: Applied and Computational Harmonic Analysis
URI: http://scholarbank.nus.edu.sg/handle/10635/104226
ISSN: 10635203
DOI: 10.1016/j.acha.2006.09.005
Appears in Collections:Staff Publications

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