  Hyperspectral imaging measures the amount of electromagnetic energy across
the instantaneous field of view at a very high resolution in hundreds or
thousands of spectral channels. This enables objects to be detected and the
identification of materials that have subtle differences between them. However,
the increase in spectral resolution often means that there is a decrease in the
number of photons received in each channel, which means that the noise linked
to the image formation process is greater. This degradation limits the quality
of the extracted information and its potential applications. Thus, denoising is
a fundamental problem in hyperspectral image (HSI) processing. As images of
natural scenes with highly correlated spectral channels, HSIs are characterized
by a high level of self-similarity and can be well approximated by low-rank
representations. These characteristics underlie the state-of-the-art methods
used in HSI denoising. However, where there are rarely occurring pixel types,
the denoising performance of these methods is not optimal, and the subsequent
detection of these pixels may be compromised. To address these hurdles, in this
article, we introduce RhyDe (Robust hyperspectral Denoising), a powerful HSI
denoiser, which implements explicit low-rank representation, promotes
self-similarity, and, by using a form of collaborative sparsity, preserves rare
pixels. The denoising and detection effectiveness of the proposed robust HSI
denoiser is illustrated using semireal and real data.
