Deep convolutional neural networks (CNNs) have contributed to the significant progress of
the single-image super-resolution (SISR) field. However, the majority of existing CNN-based …

Benefiting from the development of deep convolutional neural networks, CNN-based single-
image super-resolution methods have achieved remarkable reconstruction results …

The goal of learning-based image super resolution (SR) is to generate a plausible and
visually pleasing high-resolution (HR) image from a given low-resolution (LR) input. The SR …

Joint sparse representation (JSR) has shown great potential in various image processing
and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this …

Recovering the image corrupted by additive white Gaussian noise (AWGN) and impulse
noise is a challenging problem due to its difficulties in an accurate modeling of the …

The performance of traditional face recognition systems is sharply reduced when
encountered with a low-resolution (LR) probe face image. To obtain much more detailed …

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 …

An image may be influenced by noise during capturing and transmitting process. Removing
the possible noise from the image has always been a challenging issue due to this fact that …

Transformer-based architectures start to emerge in single image super resolution (SISR)
and have achieved promising performance. However, most existing vision Transformer …

Single image super-resolution (SISR) using deep convolutional neural networks (CNNs)
achieves the state-of-the-art performance. Most existing SISR models mainly focus on …