Blind Hyperspectral Image Restoration

A new blind restoration method for hyperspectral images is proposed in this paper. This method eliminates prior known information and avoids empirical tuning of the regularization parameters. It involves two major steps: the first step is an unsupervised partitioning using affinity propagation to blindly select spectral components and instead of estimating the PSF using all spectral components, we use only the exemplar component of each correlated group. The second step involves restoration of the hyperspectral image using the appropriate estimated PSF for each group. A multi-scale pyramidal model is used to estimate the PSF and the latent image on each selected spectral component. Hyperspectral images are used to assess the proposed method. Three objective criteria are adopted the mean of PSNR, SSIM, and L1-norm of the estimation error.