Sparse based similarity measure for mono-modal image registration

Similarity measure is an important key in image registration. Most traditional intensity-based similarity measures (e.g., SSD, CC, MI, and CR) assume stationary image and pixel by pixel independence. Hence, perfect image registration cannot be achieved especially in presence of spatially-varying intensity distortions and outlier objects that appear in one image but not in the other. Here, we suppose that non stationary intensity distortion (such as Bias field or Outlier) has sparse representation in transformation domain. Based on this as-sumption, the zero norm (ℓ0)of the residual image between two registered images in transform domain is introduced as a new similarity measure in presence of non-stationary inten-sity. In this paper we replace ℓ0 norm with ℓ1 norm which is a popular sparseness measure. This measure produces accurate registration results in compare to other similarity measure such as SSD, MI and Residual Complexity RC.

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