Efficient Global Weighted Least-Squares Translation Registration in the Frequency Domain

The weighted sum of squared differences cost function is often minimized to align two images with overlapping fields of view. If one image is shifted with respect to the other, the cost function can be written as a sum involving convolutions. This paper demonstrates that performing these convolutions in the frequency domain saves a significant amount of processing time when searching for a global optimum. In addition, the method is invariant under linear intensity mappings. Applications include medical imaging, remote sensing, fractal coding, and image photomosaics.

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