Convolutional neural networks are the most successful models in single image super-
resolution. Deeper networks, residual connections, and attention mechanisms have further …

Recently, convolutional neural network (CNN) has been widely used in single image super-
resolution (SISR) and made significant advances. However, most of the existing CNN-based …

The use of deep convolutional neural networks (CNNs) for image super-resolution (SR) from
low-resolution (LR) input has achieved remarkable reconstruction performance with the …

Convolutional neural networks have allowed remarkable advances in single image super-
resolution (SISR) over the last decade. Among recent advances in SISR, attention …

Recently, lightweight methods for single-image super-resolution have gained significant
popularity and achieved impressive performance due to limited hardware resources. These …

Transformer-based methods have demonstrated excellent performance on super-resolution
visual tasks, surpassing conventional convolutional neural networks. However, existing work …

Recent studies have shown that deep neural networks can significantly improve the quality
of single-image super-resolution. Current researches tend to use deeper convolutional …

The emergence of convolutional neural network (CNN) and transformer has recently
facilitated significant advances in image super-resolution (SR) tasks. However, these …

Single image super-resolution based on convolutional neural networks has been very
successful in recent years. However, as the computational cost is too high, making it difficult …

One non-negligible flaw of the convolutional neural networks (CNNs) based single image
super-resolution (SISR) models is that most of them are not able to restore high-resolution …