ITERATIVE FITTING AFTER ELASTIC REGISTRATION: AN EFFICIENT STRATEGY FOR ACCURATE ESTIMATION OF PARAMETRIC DEFORMATIONS (original) (raw)
2017, IEEE International Conference on Image Processing (ICIP)
We propose an efficient method for image registration based on iteratively fitting a parametric model to the output of an elastic registration. It combines the flexibility of elastic registration able to estimate complex deformations-with the robustness of parametric registration-able to estimate very large displacement. Our approach is made feasible by using the recent Local All-Pass (LAP) algorithm; a fast and accurate filter-based method for estimating the local deformation between two images. Moreover, at each iteration we fit a linear parametric model to the local deformation which is equivalent to solving a linear system of equations (very fast and efficient). We use a quadratic polynomial model however the framework can easily be extended to more complicated models. The significant advantage of the proposed method is its robustness to model mis-match (e.g. noise and blurring). Experimental results on synthetic images and real images demonstrate that the proposed algorithm is highly accurate and outperforms a selection of image registration approaches .
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