Universal HMT based super resolution for remote sensing images

In this paper, we propose a new super resolution method Maximum a Posteriori based on a universal hidden Markov tree model (MAP-uHMT) for remote sensing images. The hidden Markov tree theory in the wavelet domain is used to set up a prior model for reconstructing super resolution images from a sequence of warped, blurred, sub-sampled and contaminated low resolution images. Both the simulation results with a Landsat7 panchromatic image and actual results with four Landsat7 panchromatic images which were captured on different dates show that our method achieves better super resolution images both visually and quantitatively than other methods, based on PSNR in the simulation and derived PSF with actual data.