Recently, deep learning-based methods are proposed for hyperspectral images (HSIs)
denoising. Among them, unsupervised methods such as deep image prior (DIP)-based …

To eliminate the mixed noise in hyperspectral images (HSIs), three-dimensional total
variation (3DTV) regularization has been proven as an efficient tool. However, 3DTV …

We propose a new constrained optimization approach to hyperspectral (HS) image
restoration. Most existing methods restore a desirable HS image by solving some …

Hyperspectral image (HSI) mixed denoising is a challenging task in the fields of remote
sensing, environmental monitoring, mineral exploration, and so on. A crucial difficulty is to …

This article proposes a new high spatial resolution hyperspectral (HR-HS) image estimation
method based on convex optimization. The method assumes a low spatial resolution HS (LR …

Recently, tensor singular value decomposition (t-SVD) has emerged as a promising tool for
hyperspectral image (HSI) processing. In the t-SVD, there are two key building blocks: 1) the …

Hyperspectral (HS) imaging based on compressed sensing (CS) is actively studied to
capture an HS image in one shot. Although CS can reconstruct an HS image from a much …

Recently, convolutional neural network (CNN)-based methods are proposed for
hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as the …