- Phase correlation
In image processing,

**phase correlation**is a fastfrequency-domain approach to estimate the relative translative movement between twoimage s.**Method**Given two input images $g\_a$ and $g\_b$:

Apply a

window function (e.g., aHamming window ) on both images to reduce edge effects. Then, calculate the discrete 2D Fourier transform of both images.:$mathbf\{G\}\_a\; =\; mathcal\{F\}\{g\_a\},\; ;\; mathbf\{G\}\_b\; =\; mathcal\{F\}\{g\_b\}$

Calculate the

cross-power spectrum by taking thecomplex conjugate of the second result, multiplying theFourier transform s together elementwise, and normalizing this product elementwise.:$R\; =\; frac\{\; mathbf\{G\}\_a\; mathbf\{G\}\_b^*\}\; \backslash \; \&=\; frac\{\; mathbf\{G\}\_a\; mathbf\{G\}\_a^*\; e^\{2\; pi\; i\; (frac\{u\; Delta\; x\}\{M\}\; +\; frac\{v\; Delta\; y\}\{N\})\; \backslash \; \&=\; e^\{2\; pi\; i\; (frac\{u\; Delta\; x\}\{M\}\; +\; frac\{v\; Delta\; y\}\{N\})\; \}end\{align\}$since the magnitude of a complex exponential always is one, and the phase of $mathbf\{G\}\_a\; mathbf\{G\}\_a^*$ always is zero.

The inverse Fourier transform of a complex exponential is a

Kronecker delta , i.e. a single peak::$r(x,y)\; =\; delta(x\; +\; Delta\; x,\; y\; +\; Delta\; y)$

This result could have been obtained by calculating the

cross correlation directly. The advantage of this method is that the discrete Fourier transform and its inverse can be performed using thefast Fourier transform , which is much faster than correlation for large images.**Limitations**In practice, it is more likely that $g\_b$ will be a simple linear shift of $g\_a$, rather than a circular shift as required by the explanation above. In such cases, $r$ will not be a simple delta function, which will reduce the performance of the method. In such cases, a

window function should be employed during the Fourier transform to reduce edge effects. However, if the images consist of a flat background, with all detail situated away from the edges, then a linear shift will be equivalent to a circular shift, and the above derivation will hold exactly.For periodic images (such as a chessboard), phase correlation may yield ambiguous results with several peaks in the resulting spectrum.

**Example**The following image demonstrates the usage of phase correlation to determine relative translative movement between two images corrupted by independent Gaussian noise. One can clearly see a peak in the phase-correlation image approximately at (30,33).

**See also***

Cross correlation **References*** E. De Castro and C. Morandi " [

*http://www.idi.ntnu.no/~fredrior/files/Registration%20of%20Translated%20and%20Rotated%20Images%20Using%20FT.pdf Registration of Translated and Rotated Images Using Finite Fourier Transforms*] ", IEEE Transactions on pattern analysis and machine intelligence, Sept. 1987

*Wikimedia Foundation.
2010.*