Blind Image Fusion for Hyperspectral Imaging with Directional Total VariationMS55

We present the problem of simultaneously increasing the spatial resolution and deconvolving channels of hyperspectral images where the blurring kernels are unknown. A high resolution image is incorporated into a directional total variation prior for the corresponding variational model. The non-smoothness and non-convexity of the objective function is treated using the PALM-algorithm. Numerical results on remote sensing data show the potential of the proposed method and suggest that it is robust with respect to mis-registration.

This presentation is part of Minisymposium “MS55 - Advances of regularization techniques in iterative reconstruction (2 parts)
organized by: Zichao (Wendy) Di (Argonne National Lab) , Marc Aurèle Gilles (Cornell University) .

Leon Bungert (University of Münster)
Matthias J. Ehrhardt (University of Cambridge)
Carola-Bibiane Schönlieb (University of Cambridge)
Marc Aurèle Gilles (Cornell University)
David Coomes ( Forest Ecology and Conservation Group, Department of Plant Sciences, University of Cambridge)
Rafael Reisenhofer (University of Bremen)
Jennifer Rasch (Fraunhofer Heinrich Hertz Institute)
hyperspectral imaging, image deblurring, image fusion, inverse problems