Super resolution (SR) is an eminent system in the field of computer vison and image
processing to improve the visual perception of the poor-quality images. The key objective of …

Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …

Adapting the Diffusion Probabilistic Model (DPM) for direct image super-resolution is
wasteful, given that a simple Convolutional Neural Network (CNN) can recover the main low …

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 …

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 …

Deep convolutional networks–based super-resolution is a fast-growing field with numerous
practical applications. In this exposition, we extensively compare more than 30 state-of-the …

At present, with the advance of satellite image processing technology, remote sensing
images are becoming more widely used in real scenes. However, due to the limitations of …

Existing enhancement methods are empirically expected to help the high-level end
computer vision task: however, that is observed to not always be the case in practice. We …

High-resolution images have a wide range of applications in image compression, remote
sensing, medical imaging, public safety, and other fields. The primary objective of super …

Super-Resolution convolutional neural networks have recently demonstrated high-quality
restoration for single images. However, existing algorithms often require very deep …