Singular value decomposition based fusion for super-resolution image reconstruction

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Haidawati Binti Mohamad Nasir, H and Stankovic, Vladimir and Marshall, Stephen (2012) Singular value decomposition based fusion for super-resolution image reconstruction. Signal Processing: Image Communication, 27 (2). 180–191. ISSN 0923-5965 (https://doi.org/10.1016/j.image.2011.12.002)

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Abstract

In this paper, we address a super-resolution problem of generating a high-resolution image from low-resolution images. The proposed super-resolution method consists of three steps: image registration, singular value decomposition (SVD)-based image fusion and interpolation. The contribution of this work is twofold. First we customize an image registration approach using Scale Invariant Feature Transform (SIFT), Belief Propagation and Random Sampling Consensus (RANSAC) for super-resolution. Second, we propose SVD-based fusion to integrate the important features from the low-resolution images. The proposed image registration and fusion steps effectively maintain the important features and greatly improve the super-resolution results. Results, for a variety of image examples, show that the proposed method successfully generates high-resolution images from low-resolution images.

ORCID iDs

Haidawati Binti Mohamad Nasir, H, Stankovic, Vladimir ORCID logoORCID: https://orcid.org/0000-0002-1075-2420 and Marshall, Stephen ORCID logoORCID: https://orcid.org/0000-0001-7079-5628;
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