Hyperspectral Image Restoration Based on Salient Edges

Hyperspectral images acquired by remote sensing systems are generally degraded by noise and can be sometimes more severely degraded by blur. In this study, we address the semiblind restoration of the degraded images component-wise, according to a sequential scheme. We propose a new component-wise semi-blind method for estimating effectively and accurately both the blur and the corresponding latent image. To prove applicability and higher efficiency of the proposed method, we compare it against the method it originates from. Our attention is mainly paid to the objective analysis (via l1-norm) of the estimation error accuracy. The tests are performed on a synthetic hyperspectral image. This image has been successively degraded with eight real blurs taken from the literature, each of a different support size. Conclusions, practical recommendations and perspectives are drawn from the results experimentally obtained.