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A sensor-data-based denoising framework for hyperspectral images
Abstract : Many denoising approaches extend image processing to a hyperspectral cube structure, but do not take into account a sensor model nor the format of the recording. We propose a denoising framework for hyperspectral images that uses sensor data to convert an acquisition to a representation facilitating the noise-estimation, namely the photon-corrected image. This photon corrected image format accounts for the most common noise contributions and is spatially proportional to spectral radiance values. The subsequent denoising is based on an extended variational denoising model, which is suited for a Poisson distributed noise. A spatially and spectrally adaptive total variation regularisation term accounts the structural proposition of a hyperspectral image cube. We evaluate the approach on a synthetic dataset that guarantees a noise-free ground truth, and the best results are achieved when the dark current is taken into account.
Keywords :
Hyperspectral imaging
denoising
https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01217266
Contributeur : Alamin Mansouri <>
Soumis le : lundi 19 octobre 2015 - 11:42:22
Dernière modification le : mardi 6 octobre 2020 - 16:30:48
Contributeur : Alamin Mansouri <>
Soumis le : lundi 19 octobre 2015 - 11:42:22
Dernière modification le : mardi 6 octobre 2020 - 16:30:48
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