Image super‐resolution reconstruction refers to a technique of recovering a high‐resolution
(HR) image (or multiple images) from a low‐resolution (LR) degraded image (or multiple …

With the goal of recovering high-quality image content from its degraded version, image
restoration enjoys numerous applications, such as in surveillance, computational …

Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …

Recent works based on deep learning and facial priors have succeeded in super-resolving
severely degraded facial images. However, the prior knowledge is not fully exploited in …

Convolutional neural networks have been proven to be of great benefit for single-image
super-resolution (SISR). However, previous works do not make full use of multi-scale …

U-Net has become one of the state-of-the-art deep learning-based approaches for modern
computer vision tasks such as semantic segmentation, super resolution, image denoising …

Depth map super-resolution is an ill-posed inverse problem with many challenges. First,
depth boundaries are generally hard to reconstruct particularly at large magnification factors …

Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However,
due to the limitations in hardware, scan time, and throughput, it is often clinically challenging …

Deep neural networks (DNNs) based methods have achieved great success in single image
super-resolution (SISR). However, existing state-of-the-art SISR techniques are designed …

High-resolution (HR) medical images are preferred in clinical diagnoses and subsequent
analysis. However, the acquisition of HR medical images is easily affected by hardware …