Accuracy assessment of blind and semi-blind restoration methods for hyperspectral images
Résumé
Hyperspectral images acquired by remote sensing systems are generally degraded by noise and can be sometimes more severely degraded by blur. When no knowledge is available about the degradations present or the original image, blind restoration methods must be considered. Otherwise, when a partial information is needed, semi-blind restoration methods can be considered. Numerous semi-blind and quite advanced methods are available in the literature. So to get better insights and feedback on the applicability and potential efficiency of a representative set of four semi-blind methods recently proposed, we have performed a comparative study of these methods in objective terms of blur filter and original image error estimation accuracy. In particular, we have paid special attention to the accurate recovering in the spectral dimension of original spectral signatures. We have analyzed peculiarities and factors restricting the applicability of these methods. Our tests are performed on a synthetic hyperspectral image, degraded with various synthetic blurs (out-of-focus, gaussian, motion) and with signal independent noise of typical levels such as those encountered in real hyperspectral images. This synthetic image has been built from various samples from classified areas of a real-life hyperspectral image, in order to benefit from realistic reference spectral signatures to recover after synthetic degradation. Conclusions, practical recommendations and perspectives are drawn from the results experimentally obtained.
Mots clés
Additive noise
Constrained optimization
Image reconstruction
Independent component analysis
Optimization
Remote sensing
Restoration
Signal processing
Spectroscopy
Alternating direction method of multipliers
Alternating minimization
Augmented Lagrangians
Regularization parameters
Remote sensing images
Semi-blind
Sparse distribution
Stopping criteria
Total variation
Image processing
remote sensing images
additive noise
semi-blind image restoration
sparse distribution
alternating minimization
regularization parameter
augmented Lagrangian
alternating direction method of multipliers (ADMM)
total variation
stopping criteria
Origine | Fichiers produits par l'(les) auteur(s) |
---|
Loading...