An adaptive method for simultaneous image super-resolution and motion estimation based on nonlinear least square
基于非线性最小二乘的图像自适应SR重建以及运动估计

Image super-resolution(SR) reconstruction refers to a signal processing approach which produces high-resolution images from observed multiple low-resolution images A new method for simultaneous image super-resolution and motion estimation is proposed to expand the application range of SR technology. The framework of SR resolution and motion estimation is given based on maximum a posteriori (MAP). The framework takes into account both the influence of HR image dispersion between two iterations, and the weight of different LR images, which makes the algorithm self-adapting. The framework then can turn to SR resolution and motion estimation model. Nonlinear least squares method is employed to solve the model to get the global motion area of SR resolution. Our experimental results show the effectiveness of the proposed algorithm.