Deep neural networks have exhibited promising performance in image super-resolution
(SR) by learning a nonlinear mapping function from low-resolution (LR) images to high …

Reference-based image super-resolution (RefSR) has shown promising success in
recovering high-frequency details by utilizing an external reference image (Ref). In this task …

Recent advances in image super-resolution (SR) explored the power of deep learning to
achieve a better reconstruction performance. However, the feedback mechanism, which …

Owe to the powerful generative priors the pre-trained text-to-image (T2I) diffusion models
have become increasingly popular in solving the real-world image super-resolution …

In this paper, we present a large-scale Diverse Real-world image Super-Resolution dataset,
ie, DRealSR, as well as a divide-and-conquer Super-Resolution (SR) network, exploring the …

Most of the existing learning-based single image super-resolution (SISR) methods are
trained and evaluated on simulated datasets, where the low-resolution (LR) images are …

In most studies on learning-based image super-resolution (SR), the paired training dataset
is created by downscaling high-resolution (HR) images with a predetermined operation (eg …

Recently, the performance of single image super-resolution (SR) has been significantly
improved with powerful networks. However, these networks are developed for image SR …

Single-image super-resolution restores the lost structures and textures from low-resolved
images, which has achieved extensive attention from the research community. The top …

Recent research on super-resolution has achieved greatsuccess due to the development of
deep convolutional neu-ral networks (DCNNs). However, super-resolution of arbi-trary scale …