HYPERSPECTRAL IMAGE FUSION USING FAST HIGH-DIMENSIONAL DENOISING

In hyperspectral image fusion, a high resolution multispectral (MS) image is combined with a low resolution hyperspectral (HS) image to obtain a high resolution HS image. In this work, we propose a "plug-and-play" framework for HS-MS fusion, where the inversion step at each iteration involves the solution of a linear system, and the regularization is performed using a high-dimensional kernel denoiser. The core contribution is the design of the denoiser, which can denoise an HS-image at low complexity using clustering and convolutions. In particular, it can exploit the inter-band correlations, which cannot be done using band-by-band denoising. An important technical aspect of our denoiser is that it can be expressed as the proximal map of a proper, closed, and convex regularizer, which guarantees the convergence of the plug-and-play iterations. Preliminary results suggest that we are competitive with state-of-the-art algorithms for HS-MS fusion in terms of speed and restoration accuracy.