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 …

Most of the existing learning-based single image super-resolution (SISR) methods are
trained and evaluated on simulated datasets, where the low-resolution (LR) images are …

Real-world imagery does not always exhibit good visibility and clean content, but often
suffers from various kinds of degradations (eg, noise, blur, rain drops, fog, color distortion …

Sporadic ocean color events with characteristic spectra, in particular algal blooms, call for
quick delivery of high-resolution remote sensing data for further analysis. Motivated by this …

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 …

This paper reviewed the 3rd NTIRE challenge on single-image super-resolution (restoration
of rich details in a low-resolution image) with a focus on proposed solutions and results. The …

The aim of this paper is to propose a large scale dataset for image restoration (LSDIR).
Recent work in image restoration has focused on the design of deep neural networks. The …

The 4K content can deliver a more immersive visual experience to consumers due to the
huge improvement in spatial resolution. However, the high spatial resolution brings a great …

Whether conventional machine learning-based or current deep neural networks-based
single image super-resolution (SISR) methods, they are generally trained and validated on …

Demosaicing, denoising and super-resolution (SR) are of practical importance in digital
image processing and have been studied independently in the passed decades. Despite the …