Accurate definition of the similarity measure is a key component in image registration. Most commonly used intensitybased 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 spatially-varying intensity distortions. We propose a novel similarity measure

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... accounts for intensity nonstationarities and complex spatially-varying intensity distortions in mono-modal settings. 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. One of the key advantages of the new similarity measure is its simplicity in terms of both computational complexity and implementation. This measure produces accurate registration results on both artificial and real-world problems that we have tested, and outperforms other state-of-the-art similarity measures in these cases. Index Terms-Bias field, image registration, nonstationary intensity distortion, residual complexity, sparseness. 1 Spatial stationarity implies the equal form of the probability density function regardless of any shift in image spatial dimension. 0278-0062/$26.00