Sparsity-based approaches for multispectral super-resolution of tropical cyclone imagery

Abstract

An aperture synthesis system produces ringing at sharp edges and other transitions in the observed field. In this paper, we have developed an efficient multispectral deconvolution method, based on Split Bregman total variation minimization technique, and showed it to reduce image ringing, blurring, and distortion, while sharpening the image and preserving information content. We also present a multispectral multiframe super-resolution method that is robust to image noise and noise in the point spread function and leads to additional improvements in spatial resolution. The methodologies are based on current research in sparse optimization and compressed sensing, which lead to unprecedented efficiencies for solving image reconstruction problems.