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.
Highlights
Hyperspectral imaging (HSI) is affected by noise, which impacts the precision of all further processing steps, such as unmixing [1] or classification [2]
We investigated the influence of the dark current on the two image formats fc, Eq (7) compared to fcs, Eq (8)
The proposed photon corrected format Lc, and the true photoelectron count Lt show quit similar results. Both take the dark current into account and are notably more effective than the simplified Lcs
Summary
Hyperspectral imaging (HSI) is affected by noise, which impacts the precision of all further processing steps, such as unmixing [1] or classification [2]. Zhang et al [10] employed a low-rank matrix recovery, which can simultaneously remove Gaussian noise, impulse noise, dead pixels or lines, and stripes These current denoising approaches adapt for the type of noise and the structural properties of the image cube, but remain rather vague about the parameterisation, and do not clarify whether the HSI should be stored as a radiometric calibrated radiance values or raw sensor output. The proposed denoising framework for HSI (see Fig. 1) uses sensor data to transform the image to a photon corrected representation This format is similar to the one proposed in [11], but accounts for the contribution of the dark current and does not use a constant weighting factor.
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