Bayesian multi-frame super-resolution of differently exposed images

摘要

This paper presents a technique that performs multi-frame super-resolution of differently exposed images. The method first employs a coarse-to-fine image registration method to align image in both spatial and range domain. Then an image fusion method based on the maximum a posterior (MAP) is used to reconstruct, a high-resolution image. The MAP cost function includes a data fidelity term and a regularized term. The data fidelity term is in the L2 norm, and the regularized term employs Huber-Markov prior which can reduce the noise and artifacts while reserving image edges. In order to reduce the influence of registration errors, the high-resolution image estimate and registration parameters are refined alternatively by minimizing the cost function. Experiments with synthetic and real images show that the photometric registration reduce the grid-like artifacts in the reconstructed high-resolution image, and the proposed multi-frame super resolution method has a better performance than the interpolation-based method with lower RMSR and less artifacts.