Multiframe image super-resolution using quasi-newton algorithms

Multiframe super-resolution algorithms can be used to reconstruct a high-quality high-resolution image from several warped, blurred, undersampled, and possibly noisy images. A widely used means of implementing such algorithms is by optimization-based model inversion. In the past, steepest-descent methods have been applied. While easy to implement, these methods are known for their poor convergence properties and for being sensitive to numerical ill-conditioning. In this paper, we show that the multiframe super-resolution problem can be solved by using quasi-Newton algorithms and propose efficient implementations. Two of these algorithms were applied to a known super-resolution scheme and preliminary results obtained show a significant improvement in terms of convergence speed.