Remote sensing provides valuable information about objects and areas from a distance in
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …

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

Many imaging problems require solving an inverse problem that is ill-conditioned or ill-
posed. Imaging methods typically address this difficulty by regularizing the estimation …

Assessing the quality of a reconstructed hyperspectral image (HSI) is of significance for
restoration and super-resolution. Current image quality assessment methods such as peak …

The presence of blur is prevalent in satellite remote sensing images (RSIs), and its
detrimental impact on downstream applications cannot be overlooked. Current deep …

This paper presents a detailed theoretical analysis of the three stochastic approximation
proximal gradient algorithms proposed in our companion paper [AF Vidal et al., SIAM J …

In hyperspectral image (HSI) reconstruction tasks, due to the lack of ground truth in real
imaging processes, models are usually trained and validated on simulation datasets and …

Multispectral remote sensing images (RSI), including hyperspectral and multispectral
images, contain adequate information of ground objects and areas and play important roles …

This paper focuses on the blind unmixing technique for analyzing hyperspectral images
(HSIs). A joint deconvolution and blind hyperspectral unmixing (DBHU) algorithm is …

Coupling methods of integrating multiple priors have emerged as a pivotal research focus in
hyperspectral image (HSI) restoration. Among these methods, the Plug-and-Play (PnP) …