EM-based simultaneous registration, restoration, and interpolation of super-resolved images

A maximum likelihood (ML) solution to the problem of obtaining high-resolution images from sequences of noisy, blurred, and low-resolution images is presented. In our formulation, the registration parameters of the low-resolution images, the degrading blur, and noise variance are unknown. Our algorithm has the advantage that all unknown parameters are obtained simultaneously using all of the available data. An efficient implementation is presented in the frequency domain, based on the expectation maximization (EM) algorithm. Simulations demonstrate the effectiveness of the algorithm.