By fusing a low-resolution hyperspectral image (LrMSI) with an auxiliary high-resolution
multispectral image (HrMSI), hyperspectral image super-resolution (HISR) can generate a …

Recent progress in remote sensing image (RSI) super-resolution (SR) has exhibited
remarkable performance using deep neural networks, eg, Convolutional Neural Networks …

Heterogeneous graphs are ubiquitous in the real world. Recent methods aim to obtain
meaningful low-dimensional node embeddings from heterogeneous graphs to facilitate …

Unsupervised image restoration methods relying on a single data source often face
challenges in achieving high-quality visual data completion due to the absence of additional …

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) …

In recent years, convolutional neural network (CNN)-based and transformer-based
approaches have made strides in improving the performance of hyperspectral image (HSI) …

Pansharpening requires the fusion of a low-spatial-resolution multispectral (LRMS) image
and a panchromatic (PAN) image with rich spatial details to obtain a high-spatial-resolution …

Hyperspectral Image Fusion (HIF) aims to fuse low-resolution hyperspectral images (LR-
HSIs) and high-resolution multispectral images (HR-MSIs) to reconstruct high spatial and …

The tradeoff between spatial and spectral resolution in sensor design is inevitable, and
spatial–spectral fusion aims to use low spatial resolution hyperspectral image (HSI) and …

Deep learning has achieved significant success in single hyperspectral image super-
resolution (SHSR); however, the high spectral dimensionality leads to a heavy …