Single image super-resolution, as a high dimensional structured prediction problem, aims to
characterize fine-grain information given a low-resolution sample. Recent advances in …

Recent advances in single image super-resolution (SISR) have achieved extraordinary
performance, but the computational cost is too heavy to apply in edge devices. To alleviate …

While lightweight ViT framework has made tremendous progress in image super-resolution,
its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme …

Traditional super-resolution (SR) methods by minimize the mean square error usually
produce images with over-smoothed and blurry edges, due to the lack of high-frequency …

We propose an effective lightweight dynamic local and global self-attention network
(DLGSANet) to solve image super-resolution. Our method explores the properties of …

Recently, it has been demonstrated that deep neural networks can significantly improve the
performance of single image super-resolution (SISR). Numerous studies have concentrated …

Recently, deep convolutional neural networks (CNNs) have been widely explored in single
image super-resolution (SISR) and obtained remarkable performance. However, most of the …

Efficient and effective real-world image super-resolution (Real-ISR) is a challenging task
due to the unknown complex degradation of real-world images and the limited computation …

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