Acquainted Non-convexity Multiresolution Based Optimization for Affine Parameter Estimation in Image Registration

Abstract

Affine parameter estimation technique applied to image registration is found useful in obtaining reliable fusion of same object’s images taken from different modalities, into single image with strong features. Usually, the minimization in affine parameter estimation technique can be done by least squares in a quadratic way. However, this will be sensitive to the presence of outliers. Therefore, affine parameter estimation technique for image registration calls for methods that are robust enough to withstand the influence of outliers. Progressively, some robust estimation techniques demanding non-quadratic and non-convex potentials adopted from statistical literature have been used for solving these. Addressing the minimization of error function in a factual framework for finding the global optimal solution, the minimization can begin with the convex estimator at the coarser level and gradually introduce non-convexity i.e., from soft to hard redescending non-convex estimators when the iteration reaches finer level of multiresolution pyramid. Comparison has been made to find the performance results of proposed method with the registration results found using different robust estimators.