High resolution magnetic resonance (MR) imaging is desirable in many clinical applications
due to its contribution to more accurate subsequent analyses and early clinical diagnoses …

Spatial resolution is a critical imaging parameter in magnetic resonance imaging. The image
super-resolution (SR) is an effective and cost efficient alternative technique to improve the …

High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical
information important for clinical application and quantitative image analysis. However, HR …

High-resolution (HR) magnetic resonance imaging (MRI) provides detailed anatomical
information that is critical for diagnosis in the clinical application. However, HR MRI typically …

High-quality high-resolution (HR) magnetic resonance (MR) images afford more detailed
information for reliable diagnosis and quantitative image analyses. Deep convolutional …

Deep learning methods have shown outstanding performance in many applications,
including single-image super-resolution (SISR). With residual connection architecture …

In recent years, deep learning techniques have significantly improved the performance of
single image super-resolution (SISR). However, this improvement is often achieved at the …

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 …

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 successfully applied to the
single-image super-resolution (SISR) task with great improvement in terms of both peak …