Recent efforts have witnessed remarkable progress in satellite video super-resolution
(SVSR). However, most SVSR methods usually assume the degradation is fixed and known …

Existing convolutional neural networks (CNN) based image super-resolution (SR) methods
have achieved impressive performance on bicubic kernel, which is not valid to handle …

Image super-resolution (SR) research has witnessed impressive progress thanks to the
advance of convolutional neural networks (CNNs) in recent years. However, most existing …

Degradation models play an important role in Blind super-resolution (SR). The classical
degradation model, which mainly involves blur degradation, is too simple to simulate real …

Most conventional supervised super-resolution (SR) algorithms assume that low-resolution
(LR) data is obtained by downscaling high-resolution (HR) data with a fixed known kernel …

Most of the existing blind image Super-Resolution (SR) methods assume that the blur
kernels are space-invariant. However, the blur involved in real applications are usually …

In this paper, we tackle the problem of blind image super-resolution (SR) with a reformulated
degradation model and two novel modules. Following the common practices of blind SR, our …

Previous convolutional neural network (CNN) based blind super-resolution (SR) methods
usually adopt an iterative optimization way to approximate the ground-truth (GT) step-by …

Previous deep learning-based super-resolution (SR) methods rely on the assumption that
the degradation process is predefined (eg, bicubic downsampling). Thus, their performance …

Deep learning based methods have dominated super-resolution (SR) field due to their
remarkable performance in terms of effectiveness and efficiency. Most of these methods …