Nonrigid Image Deformation Using Moving Regularized Least Squares

This letter presents an image deformation method based on Moving Regularized Least Squares optimization. The user controls the deformation by simply choosing a set of point handles in the input image, and also the target positions that the source point handles should be deformed to. The deformation function in our method is nonrigid and specified in a functional space, more specifically a reproducing kernel Hilbert space. The proposed method possesses three characteristics: 1) it is able to create detail-preserving and intuitive deformations; 2) the solution of the deformation function has a simple closed-form; 3) it is extremely computationally efficient which can be performed in real-time (less than 0.1 milliseconds per frame for an image of size 500 ×500). We compare our method to a state-of-the-art method which is modeled by rigid transformations; the qualitative and quantitative results demonstrate the benefits of using the nonrigid formulation in aspects of both accuracy and efficiency. Moreover, the proposed method is general and it can be applied to other applications for interpolation.