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Towards a blind restoration method of hyperspectral images
Author(s): Mo Zhang; Benoit Vozel; Kacem Chehdi; Mykhail Uss; Sergey Abramov; Vladimir Lukin
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Paper Abstract

Image restoration is a necessary stage in the processing of remotely sensed hyperspectral images, when they are severely degraded by blur and noise. We address the semi-blind restoration of such degraded images component-wise, according to a sequential scheme. By semi-blind, we mean introducing a minimum of a priori knowledge on main unknowns in the restoration process. For each degraded component image, main unknowns are the point spread function of the blur, the original component image and the noise level. Then, the sequential component-wise scheme amounts in a first stage to estimating the blur point spread function directly from the considered degraded component image and in a second and final stage, deconvolving the degraded channel by using the PSF previously estimated. Our contribution is to improve further the sequential component-wise semi-blind variants of a recently proposed method. In this work, modifications previously introduced separately are applied all together. All these modifications together are beneficial as they tend to make the newly proposed method as independent as possible of the data content and their degradations. The resulting method is experimentally compared against its original version and the best ADMM-based alternative found experimentally in previous works. The tests are performed on three real Specim-AISA-Eagle hyperspectral images. The component images of these images are degraded synthetically with eight real and arbitrary blurs. Our attention is mainly paid to the objective analysis of the l1-norm of the estimation errors. Experimental results of this comparative analysis show that the newly proposed method exhibits interesting competitive performances and can outperform the methods involved in the experimental comparison.

Paper Details

Date Published: 9 October 2018
PDF: 12 pages
Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 107890A (9 October 2018); doi: 10.1117/12.2325456
Show Author Affiliations
Mo Zhang, Univ. de Rennes 1, IETR, CNRS (France)
Benoit Vozel, Univ. de Rennes 1, IETR, CNRS (France)
Kacem Chehdi, Univ. de Rennes 1, IETR, CNRS (France)
Mykhail Uss, National Aerospace Univ. (Ukraine)
Sergey Abramov, National Aerospace Univ. (Ukraine)
Vladimir Lukin, National Aerospace Univ. (Ukraine)


Published in SPIE Proceedings Vol. 10789:
Image and Signal Processing for Remote Sensing XXIV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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