Singular value decomposition based fusion for super-resolution image reconstruction

H Haidawati Binti Mohamad Nasir, Vladimir Stankovic, Stephen Marshall

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)180–191
Number of pages12
JournalSignal Processing: Image Communication
Issue number2
Publication statusPublished - Feb 2012


  • super-resolution
  • image fusion
  • high-resolution image
  • image resolution


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