A minimax-constrained superresolution algorithm for remote sensing imagery

Superresolution algorithms use several blurred, undersampled and noisy images of a scene to reconstruct a higher resolution version. In this paper we apply the superresolution concept to the remote sensing scenario, and develop a novel superresolution algorithm based on quadratic programming, and compare it with existing methods. The proposed algorithm achieves PSNR performance similar to state-of-the-art techniques, providing additional capabilities in terms of uniqueness of the solution and user-defined bounds for the superresolution problem.