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Accurate definition of similarity measure is a key component in image registration. Most commonly used intensity-based similarity measures rely on the assumptions of independence and stationarity of the intensities from pixel to pixel. Such measures cannot capture the complex interactions among the pixel intensities, and often result in less satisfactory registration performances, especially in the presence of nonstationary intensity distortions. We propose a novel similarity measure that accounts for intensity non-stationarities and complex spatially-varying intensity distortions. We derive the similarity measure by analytically solving for the intensity correction field and its adaptive regularization. The final measure can be interpreted as one that favors a registration with minimum compression complexity of the residual image between the two registered images. This measure produces accurate registration results on both artificial and real-world problems that we have tested, whereas many other state-of-the-art similarity measures have failed to do so.