Artificial intelligence (AI) plays a growing role in remote sensing (RS). Applications of AI,
particularly machine learning algorithms, range from initial image processing to high-level …

Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Hyperspectral imaging is a non-destructive, nonpolluting, and fast technology, which can
capture up to several hundred images of different wavelengths and offer relevant spectral …

Deep learning has been proved to be an advanced technology for big data analysis with a
large number of successful cases in image processing, speech recognition, object detection …

Hyperspectral (HS) imaging, also known as image spectrometry, is a landmark technique in
geoscience and remote sensing (RS). In the past decade, enormous efforts have been made …

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 …

Non-local low-rank tensor approximation has been developed as a state-of-the-art method
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …

Hyperspectral image (HSI) denoising plays an important role in image quality improvement
and related applications. Convolutional neural network (CNN)-based image denoising …

Hyperspectral Image (HSI) reconstruction has made gratifying progress with the deep
unfolding framework by formulating the problem into a data module and a prior module …