A sensor-data-based denoising framework for hyperspectral images

Ferdinand Deger, Alamin Mansouri, Marius Pedersen, Jon Y. Hardeberg, Yvon Voisin
2015 Optics Express  
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 photoncorrected image. This photon corrected image format accounts for the most common noise contributions and is spatially proportional to spectral radiance values.
more » ... 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.
doi:10.1364/oe.23.001938 pmid:25836066 fatcat:56z6lwohe5f2toymt63oahkr4y