Gradient-based subspace phase correlation for fast and effective image alignment

Jinchang Ren, Theodore Vlachos, Yi Zhang, J. zheng, Jianmin Jiang

Research output: Contribution to journalArticle

21 Citations (Scopus)
124 Downloads (Pure)


Phase correlation is a well-established frequency domain method to estimate rigid 2-D translational motion between pairs of images. However, it suffers from interference terms such as noise and non-overlapped regions. In this paper, a novel variant of the phase correlation approach is proposed, in which 2-D translation is estimated by projection-based subspace phase correlation (SPC). Conventional wisdom has suggested that such an approach can only amount to a compromise solution between accuracy and efficiency. In this work, however, we prove that the original SPC and the further introduced gradient-based SPC can provide robust solution to zero-mean and non-zero-mean noise, and the latter is also used to model the interference term of non-overlapped regions. Comprehensive results from synthetic data and MRI images have fully validated our methodology. Due to its substantially lower computational complexity, the proposed method offers additional advantages in terms of efficiency and can lend itself to very fast implementations for a wide range of applications where speed is at a premium.
Original languageEnglish
Pages (from-to)1558-1565
Number of pages8
JournalJournal of Visual Communication and Image Representation
Issue number7
Early online date14 Jul 2014
Publication statusPublished - Oct 2014


  • Image registration
  • sub-pixel alignment
  • phase correlation
  • interference terms
  • subspace projection
  • fourier transform

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