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 …

Deep convolutional neural networks (CNNs) have pushed forward the frontier of super-
resolution (SR) research. However, current CNN models exhibit a major flaw: they are …

Abstract Generative Adversarial Networks (GANs) have been widely used to recover vivid
textures in image super-resolution (SR) tasks. In particular one discriminator is utilized to …

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

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 …

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 …

A longstanding challenge in Super-Resolution (SR) is how to efficiently enhance high-
frequency details in Low-Resolution (LR) images while maintaining semantic coherence …

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 …

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

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