Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that
aims to obtain a high-resolution output from one of its low-resolution versions. Recently …

The aim of multi-output learning is to simultaneously predict multiple outputs given an input.
It is an important learning problem for decision-making since making decisions in the real …

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks,
are receiving extensive attention for their powerful capability in learning node …

The production of a high spatial resolution (HR) hyperspectral image (HSI) through the
fusion of a low spatial resolution (LR) HSI with an HR multispectral image (MSI) has …

Remote sensing image super-resolution has always been a major research focus, and many
deep-learning-based algorithms have been proposed in recent years. However, since the …

The image labeling task of remote sensing image scene classification (RSSC) is based on
the semantic content of remote sensing images. The semantic information within remote …

The increasing number of Internet of Things (IoT) objects has been a growing challenge of
the current spectrum supply. To handle this issue, the IoT devices should have cognitive …

Sparse representation based synthetic aperture radar (SAR) imaging approaches have
shown their superior performance and great potential in compressed sensing SAR imaging …

Face hallucination technologies have been widely developed during the past decades,
among which the sparse manifold learning (SML)-based approaches have become the …

In recent years, sparse unmixing has attracted significant attention, as it can effectively avoid
the bottleneck problems associated with the absence of pure pixels and the estimation of the …