For the past two decades, there have been significant efforts to develop methods for object
detection in Remote Sensing (RS) images. In most cases, the datasets for small object …

Video super-resolution (VSR) is reconstructing high-resolution videos from low resolution
ones. Recently, the VSR methods based on deep neural networks have made great …

CNNs with strong learning abilities are widely chosen to resolve super-resolution problem.
However, CNNs depend on deeper network architectures to improve performance of image …

The clarity of medical images is crucial for doctors to identify and diagnose different
diseases. High-resolution images have more detailed information and clearer content than …

This paper deals with detecting small objects in remote sensing images from satellites or
any aerial vehicle by utilizing the concept of image super-resolution for image resolution …

Super-resolution (SR) reconstruction is a hot topic in medical image processing. SR implies
reconstructing corresponding high-resolution (HR) images from observed low-resolution …

Image inpainting techniques have shown promising improvement with the assistance of
generative adversarial networks (GANs) recently. However, most of them often suffered from …

The video super-resolution (VSR) task aims to restore a high-resolution (HR) video frame by
using its corresponding low-resolution (LR) frame and multiple neighboring frames. At …

Many datasets used to train artificial intelligence systems to recognize potholes, such as the
challenging sequences for autonomous driving (CCSAD) and the Pacific Northwest road …

Vision Transformer have achieved impressive performance in image super-resolution.
However, they suffer from low inference speed mainly because of the quadratic complexity …