A new multiframe super-resolution based on nonlinear registration and a spatially weighted regularization

Abstract

Increasing the size of an image is an extensively studied problem in image processing. In recent years, many studies have been conducted on image super-resolution (SR). Since the super-resolution techniques depend on the motion precision estimation, we investigate the use of a nonlinear elastic (called hyperelastic) image registration. Also, we propose a spatially weighted second order SR algorithm, which takes into account the distribution of the spatial information in different image areas. The hyperelastic image registration is used to handle the subpixel errors between the unregistered images, while the spatially weighted second order regularization allows to increase the robustness of the restoration step with respect to degradation factors (blur and noise). As a result, the registration model is more efficient and easier to implement and the proposed SR algorithm reduces artifacts in flat regions of the image and also preserves well sharp edges. The efficiency of the proposed model is demonstrated using simulated and real tests, while comparison with other competitive SR methods is achieved.