Stacked autoencoders (SAEs), as part of the deep learning (DL) framework, have been
recently proposed for feature extraction in hyperspectral remote sensing. With the help of …

Sparse coding has been widely applied to learning-based single image super-resolution
(SR) and has obtained promising performance by jointly learning effective representations …

Rice lodging identification relies on manual in situ assessment and often leads to a
compensation dispute in agricultural disaster assessment. Therefore, this study proposes a …

Face image super-resolution has attracted much attention in recent years. Many algorithms
have been proposed. Among them, sparse representation (SR)-based face image super …

By using a sparse representation or low-rank representation of data, the graph-based
subspace clustering has recently attracted considerable attention in computer vision, given …

In this paper, a new framework for tensor hyperspectral target detection is proposed. In this
new framework, tensor is well integrated into the conventional target detection algorithm. As …

The computer graphics and computer vision communities have been working closely
together in recent years, and a variety of algorithms and applications have been developed …

In this paper, we propose a novel fine-grained dictionary learning method for image
classification. To learn a high-quality discriminative dictionary, three types of multispecific …

In human-machine interaction, human face is one of the core factors. However, due to the
limitations of imaging conditions and low-cost imaging sensors, the captured faces are often …

Matrix completion (MC) aims at recovering missing entries, given an incomplete matrix.
Existing algorithms for MC are mainly designed for noiseless or Gaussian noise scenarios …