Recently, GAN has successfully contributed to making single-image super-resolution (SISR)
methods produce more realistic images. However, natural images have complex distribution …

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 …

Recent works with an implicit neural function shed light on representing images in arbitrary
resolution. However, a standalone multi-layer perceptron shows limited performance in …

Learning continuous image representations is recently gaining popularity for image super-
resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary …

Despite breakthrough advances in image super-resolution (SR) with convolutional neural
networks (CNNs), SR has yet to enjoy ubiquitous applications due to the high computational …

Abstract We propose Super-resolution Neural Operator (SRNO), a deep operator learning
framework that can resolve high-resolution (HR) images at arbitrary scales from the low …

The 360deg imaging has recently gained much attention; however, its angular resolution is
relatively lower than that of a narrow field-of-view (FOV) perspective image as it is captured …

In recent years, deep convolutional neural networks (CNNs) have been widely employed in
image super-resolution. Thanks to the power of deep CNNs, the reconstruction performance …

The ability of scale-equivariance processing blocks plays a central role in arbitrary-scale
image super-resolution tasks. Inspired by this crucial observation, this work proposes two …

Image restoration tasks have witnessed great performance improvement in recent years by
developing large deep models. Despite the outstanding performance, the heavy …