Most existing CNN-based super-resolution (SR) methods are developed based on an
assumption that the degradation is fixed and known (eg, bicubic downsampling). However …

Super-resolution (SR) is an essential class of low-level vision tasks, which aims to improve
the resolution of images or videos in computer vision. In recent years, significant progress …

Recently, very deep convolutional neural networks (CNNs) have shown great power in
single image super-resolution (SISR) and achieved significant improvements against …

This work aims at designing a lightweight convolutional neural network for image super
resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective …

Blind image super-resolution (SR), aiming to super-resolve low-resolution images with
unknown degradation, has attracted increasing attention due to its significance in promoting …

Abstract Generative Adversarial Networks (GAN) have demonstrated the potential to recover
realistic details for single image super-resolution (SISR). To further improve the visual …

The evolution of automotive technology will eventually permit the automated driving system
on the vehicle to handle all circumstances. Human occupants will be just passengers. This …

Most consumer-grade digital cameras can only capture a limited range of luminance in real-
world scenes due to sensor constraints. Besides, noise and quantization errors are often …

Abstract Space-time video super-resolution (STVSR) aims to increase the spatial and
temporal resolutions of low-resolution and low-frame-rate videos. Recently, deformable …

Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from
photographic defects such as over/under exposure, poor contrast, inharmonious saturation …