Low-resolution face recognition (LRFR) has received increasing attention over the past few
years. Its applications lie widely in the real-world environment when high-resolution or high …

Abstract Representation learning methods have attracted considerable attention for learning-
based face super-resolution in recent years. Conventional methods perform local models …

Many successful algorithms for analyzing ECG signals leverage data-driven models that are
learned for each specific user. Unfortunately, a few algorithmic challenges are still to be …

In practical applications, due to a long distance between the monitored population and
monitoring equipment, the face images or human pose captured by the cameras often incur …

Identifying tiny objects from extremely low-resolution (LR) unmanned-aerial-vehicle-based
remote sensing images is generally considered as a very challenging task, because of very …

Spatial resolution enhancement is a pre-requisite for integrating unmanned aerial vehicle
(UAV) datasets with the data from other sources. However, the mobility of UAV platforms …

In spite of great success in many image recognition tasks achieved by recent deep models,
directly applying them to recognize low-resolution images may suffer from low accuracy due …

Most multi-view subspace clustering algorithms construct the affinity matrix with shallow
features extracted from each view separately. The integration of multi-view features are left …

In recent years, impressive advances have been made in single-image super-resolution.
Deep learning is behind much of this success. Deep (er) architecture design and external …

Identifying tiny objects with extremely low resolution is generally considered a very
challenging task even for human vision, due to limited information presented inside the …