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 …

As a special type of transformer, vision transformers (ViTs) can be used for various computer
vision (CV) applications. Convolutional neural networks (CNNs) have several potential …

Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing
the great power of deep learning, existing HSI denoising methods suffer from limitations in …

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the
subsequent HSI applications. Unfortunately, though witnessing the development of deep …

The total variation (TV) regularizer is a widely used technique in image-processing tasks to
model an image's local smoothness property. Intrinsically, the TV regularizer imposes …

Denoising is a crucial preprocessing step for hyperspectral images (HSIs) due to noise
arising from intraimaging mechanisms and environmental factors. Long-range spatial …

Remote sensing technology has undeniable importance in various industrial applications,
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …

Hyperspectral image (HSI) restoration aims at recovering clean images from degraded
observations and plays a vital role in downstream tasks. Existing model-based methods …

Hyperspectral images (HSIs) often suffer from noise arising from both intraimaging
mechanisms and environmental factors. Leveraging domain knowledge specific to HSIs …

Denoising of hyperspectral image (HSI) is challenging, especially when dealing with large-
scale data. Model-based methods show promise in HSI denoising due to their good …