Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications.
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …

The ability to capture fine spectral discriminative information enables hyperspectral images
(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the …

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the
performance of the subsequent HSI interpretation and applications. In this paper, a novel …

Although deep neural networks (DNNs) have been widely applied to hyperspectral image
(HSI) denoising, most DNN-based HSI denoising methods are designed by stacking …

The tensor tubal rank, defined based on the tensor singular value decomposition (t-SVD),
has obtained promising results in hyperspectral image (HSI) denoising. However, the …

In this article, we propose an alternating directional 3-D quasi-recurrent neural network for
hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge …

Mixed noise (such as Gaussian, impulse, stripe, and deadline noises) contamination is a
common phenomenon in hyperspectral imagery (HSI), greatly degrading visual quality and …

Recently, the low-rank and sparse decomposition model (LSDM) has been used for
anomaly detection in hyperspectral imagery. The traditional LSDM assumes that the sparse …

Videos captured by outdoor surveillance equipments sometimes contain unexpected rain
streaks, which brings difficulty in subsequent video processing tasks. Rain streak removal …

The decrease in the widths of spectral bands in hyperspectral imaging leads to a decrease
in signal-to-noise ratio (SNR) of measurements. The decreased SNR reduces the reliability …