For magnetic resonance (MR) images sharing visual characteristics, the internal structure
repetitions of different scales are considerable image-specific priors. Following the …

High-quality magnetic resonance (MR) images afford more detailed information for reliable
diagnoses and quantitative image analyses. Given low-resolution (LR) images, the deep …

Deep convolutional neural networks (CNNs) have boosted the performance of image super-
resolution (SR) in recent years. However, existing deep CNN-based SR approaches often …

Image super-resolution (SR) is a fundamental technique in the field of image processing and
computer vision. Recently, deep learning has witnessed remarkable progress in many super …

Efficient image super-resolution (SR), being preferred in the resource-constrained
scenarios, aims at not only higher super-resolving accuracy but also lower computational …

Magnetic resonance images (MRI) with high spatial resolution provide detailed anatomical
information for accurate disease diagnosis and quantitative analysis. However, the …

With its powerful ability to model geometric transformations, the deformable convolutional
network brings great improvements for single image super-resolution (SISR). Nevertheless …

The rapid development of deep learning (DL) has driven single image super-resolution (SR)
into a new era. However, in most existing DL based image SR networks, the information …

The tradeoff between efficiency and model size of the convolutional neural network (CNN) is
an essential issue for applications of CNN-based algorithms to diverse real-world tasks …

Recently, deep convolutional neural networks (CNNs) have been widely explored in single
image super-resolution (SISR) and obtained remarkable performance. However, most of the …