Image super-resolution research recently has been dominated by transformer models which
need higher computational resources than CNNs due to the quadratic complexity of self …

Abstract Recently, Convolutional Neural Network (CNN) based approaches have achieved
impressive single image super-resolution (SISR) performance in terms of accuracy and …

Deep learning techniques have been successfully applied in many areas of computer vision,
including low-level image restoration problems. For image super-resolution, several models …

This paper proposes a novel Attention-based Multi-Reference Super-resolution network
(AMRSR) that, given a low-resolution image, learns to adaptively transfer the most similar …

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 …

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

Deep learning-based single image super-resolution (SISR) approaches have drawn much
attention and achieved remarkable success on modern advanced GPUs. However, most …

Abstract Deep Convolutional Neural Networks (CNNs) have achieved remarkable results on
Single Image Super-Resolution (SISR). Despite considering only a single degradation …

Deep convolutional neural networks, particularly large models with large kernels (3× 3 or
more), have achieved significant progress in single image super-resolution (SISR) tasks …

Convolutional neural networks have been proven to be of great benefit for single-image
super-resolution (SISR). However, previous works do not make full use of multi-scale …