Image registration via low-rank factorization and maximum rank resolving

Abstract

The feature-based image registration method has a better performance in terms of robustness to the intensity variance, but its accuracy of the feature-based image registration still could be improved. This paper utilizes the low-rank factorization and maximum rank resolving to improve the accuracy of image registration. In detail, the proposed method extracts coarse geometrical transform parameters based on the feature point pairs between images, then constructs low-rank model to optimize the geometrical transform parameters and estimate the inliers. Finally, an iterative optimization strategy is introduced to acquire the optimized transform parameters by maximum rank resolving. Experimental results illustrate that the proposed approach presents a good performance in terms with the root residual mean squares error and the entropy of image difference.