Block-Coordinate Gauss–Newton Optimization and Constrained Monotone Regression for Image Registration in the Presence of Outlier Objects

In this paper, we propose the block-coordinate Gauss-Newton/regression method in order to conduct a correlation-based registration considering the intensity difference between images in the presence of outlier objects. In the proposed method, the parameters are decomposed into two blocks, one of which is for the spatial registration and the other for the intensity compensation. The two blocks are sequentially updated by the Gauss-Newton update and the polynomial regression, respectively. Because of the separated blocks, we can perform a joint optimization with low computational complexity and high implementation flexibility. For example, we apply separately appropriate scaling techniques to the parameter blocks for a stable and fast convergence of the algorithm. Furthermore, we apply the constrained monotone regression with a robust outlier detection scheme for the intensity compensation block. From numerical results, it is shown that the proposed algorithm more effectively performs a correlation-based registration considering the intensity difference alleviating the influence of the outlier objects compared to the traditional registration algorithms that perform the joint optimization.