Motion blur is a common photography artifact in dynamic environments that typically comes
jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on …

Convolutional neural networks based single-image superresolution (SISR) has made great
progress in recent years. However, it is difficult to apply these methods to real-world …

In this paper, we tackle the problem of single image dehazing with a convolutional neural
network. Within this network, we develop a multi-level fusion module to utilize both low-level …

Current super-resolution networks typically reduce network parameters and multiadds
operations by designing lightweight structures, but lightening the convolution layer is often …

Single-image super-resolution (SISR) is an important task in image processing, which aims
to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and …

Image restoration under severe weather is a challenging task. Most of the past works
focused on removing rain and haze phenomena in images. However, snow is also an …

High-resolution (HR) remote sensing imagery plays a critical role in remote sensing image
interpretation, and single image super-resolution (SISR) reconstruction technology is …

At present, super-resolution algorithms are employed to tackle the challenge of low image
resolution, but it is difficult to extract differentiated feature details based on various inputs …

At present, most mainstream algorithms for single image super-resolution (SISR) assume
the image degradation process as an ideal degradation process (eg bicubic downscaling) …

Single image super-resolution (SISR) using deep convolutional neural networks (CNNs)
achieves the state-of-the-art performance. Most existing SISR models mainly focus on …