Robust and efficient image alignment with spatially varying illumination models

Image alignment is one of the most important task in computer vision. In this paper, we explicitly model spatial illumination variations by low-order polynomial functions in an energy minimization framework. Data constraints for the alignment and illumination parameters are derived from the first-order Taylor approximation of a generalized brightness assumption. We formulate the parameter estimation problem in a weighted least-square framework by using the influence function from robust estimation to derive an iterative re-weighted least-square algorithm. A dynamic weighting scheme, which combines the factors from influence function, consistency of image gradients and nonlinear image intensity sensing, is used to improve the robustness of the image matching. In addition, a constraint sampling scheme and an estimation-warping alternative strategy are used in the proposed algorithm to improve its efficiency and accuracy. Experimental results are shown to demonstrate the robustness, efficiency and accuracy of the algorithm.