An adaptive spatial–spectral total variation approach for Poisson noise removal in hyperspectral images
Résumé
Poisson distributed noise, such as photon noise, is an important noise source in multi- and hyperspectral images. We propose a variational-based denoising approach that accounts the vectorial structure of a spectral image cube, as well as the Poisson distributed noise. For this aim, we extend an approach initially developed for monochromatic images, by a regularisation term, which is spectrally and spatially adaptive and preserves edges. In order to take the high computational complexity into account, we derive a split Bregman optimisation for the proposed model. The results show the advantages of the proposed approach compared with a marginal approach on synthetic and real data.
Mots clés
Adaptive total variation
Hyperspectral images
Poisson noise
Noise Corrupted Image
Original Hyperspectral Image
Tv Denoising
Fixed Pattern Noise
Photon Noise
Bregman Iteration
Tv Model
Synthetic Image
Regularisation TermROF Model
Poisson Noise
Monochromatic Image
Image Gradient Magnitude
Gaussian Noise Model
Noise-free Image