Image registration is an indispensable operation in image processing. To solve the problem of nonlinear distortion which easily causes low registration property, a registration scheme including a nonlinear scale space, a new discrete orthogonal Gaussian–Krawtchouk (GK) descriptor vector, and a dual similarity measurement of distance-cosine is proposed. Nonlinear scale space is constructed to preserve a lot of important structures for increasing the quantity of the salient keypoints. Meanwhile, considering both blur stability and simple computation, multiscale Harris algorithm on the basis of nonlinear scale space is applied to extract the keypoints. Moreover, according to different scales, a descriptor vector based on multiscale GK invariant moments is presented and applied to the following image matching. Finally, a dual similarity measurement of the initial Euclidean distance and the fine space cosine is proposed for the purpose of enhancing the matching accuracy. The effectiveness of the proposed algorithm has been tested on the standard images with different types of variations. The experimental results show that the performance of feature extraction and the correct matching rate of the proposed scheme are superior to those of other classical methods. The proposed scheme has a better robustness and a higher registration accuracy.