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 …

In recent years, methods based on convolutional neural network (CNN) have been the
mainstream in single image super-resolution (SISR). Although these methods have …

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 …

This work aims at designing a lightweight convolutional neural network for image super
resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective …

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

In order to solve the problem of deploying super-resolution technology on resource-limited
devices, this paper explores the differences in performance and efficiency between …

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

Recent advances in convolutional neural networks have demonstrated impressive
reconstruction for single image super-resolution (SR). However, most of the existing …

The recent advances in deep convolutional neural networks (DCNNs) have convincingly
demonstrated high-capability reconstruction for single image super-resolution (SR) …

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