Abstract
In this paper, a novel maximum a posteriori (MAP) super resolution (SR) algorithm is proposed. This algorithm estimates the registration parameters, the regularization parameter and the high resolution (HR) image simultaneously. The hyperparameters in the prior distributions of image and noise are regarded as random values, of which the ratio is the regularization parameter. By modeling the image, noise and hyperparameters correctly, the cost function is convex and each parameter has unique stable solution. In order to enhance the real time of SR algorithm, a fast block matching registration algorithm is proposed. The registration algorithm not only yields a dense motion field but also makes full use of the prior information in all low resolution (LR) images. Synthetic and real experimental results demonstrate the effectiveness of the algorithm as well as its superiority over conventional SR methods.
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