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 …

Recent advancements in light field super-resolution (SR) have yielded impressive results. In
practice, however, many existing methods are limited by assuming fixed degradation …

Conventional wisdom in blind super-resolution (SR) first estimates the unknown degradation
from the low-resolution image and then exploits the degradation information for image …

Blind single image super-resolution (SISR) is a challenging task in image processing due to
the ill-posed nature of the inverse problem. Complex degradations present in real life …

To achieve promising results on blind image super-resolution (SR), some attempts
leveraged the low resolution (LR) images to predict the kernel and improve the SR …

Current deep learning methods for single hyperspectral image super-resolution are non-
blind ones, which adopt the simplistic bicubic degradation model. These models have poor …

A hybrid architecture model of multi-branch aware CNN and efficient transformer (MAET) is
proposed and implemented for lightweight image super-resolution (SR). In the model, the …

Diffusion models (DM) have achieved remarkable promise in image super-resolution (SR).
However, most of them are tailored to solving non-blind inverse problems with fixed known …

Perceptual image encryption degrades image quality by selectively encrypting some key
information of the plain images. The encrypted images are partially perceptible according to …

Super-resolution has achieved remarkable results in recent years, which is attributed to the
rapid development of convolutional neural networks (CNN). However, most CNN-based …