While scale-invariant modeling has substantially boosted the performance of visual
recognition tasks, it remains largely under-explored in deep networks based image …

The reconstruction of a high resolution image given a low resolution observation is an ill-
posed inverse problem in imaging. Deep learning methods rely on training data to learn an …

Single image super-resolution (SISR) aims to recover the high-resolution (HR) image from
its low-resolution (LR) input image. With the development of deep learning, SISR has …

In remote sensing images (RSIs), the visual characteristics of different regions are versatile,
which poses a considerable challenge to single image super-resolution (SISR). Most …

In recent years, single image super-resolution (SR) methods based on deep convolutional
neural networks (CNNs) have made significant progress. However, due to the non-adaptive …

We propose a novel method to use both audio and a low-resolution image to perform
extreme face super-resolution (a 16x increase of the input size). When the resolution of the …

Recently, the rapid development of 2-dimensional (2D) convolutional neural networks
(CNNs) has driven single image super-resolution (SISR) into a new era, owing to their …

Recently, attention mechanisms have shown a developing tendency toward convolutional
neural network (CNN), and some representative attention mechanisms, ie, channel attention …

Text image, the one with its content dominated by text, is a common type of images seen in
many applications. In practice, text images are often degraded by many factors mixed …

Generative adversarial networks (GANs) have been widely used to single image
superresolution (SR)(SISR), and these GAN-based methods have achieved significant …