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 texture from multiple reference images to the
super-resolution output whilst maintaining spatial coherence. The use of
multiple reference images together with attention-based sampling is
demonstrated to achieve significantly improved performance over
state-of-the-art reference super-resolution approaches on multiple benchmark
datasets. Reference super-resolution approaches have recently been proposed to
overcome the ill-posed problem of image super-resolution by providing
additional information from a high-resolution reference image. Multi-reference
super-resolution extends this approach by providing a more diverse pool of
image features to overcome the inherent information deficit whilst maintaining
memory efficiency. A novel hierarchical attention-based sampling approach is
introduced to learn the similarity between low-resolution image features and
multiple reference images based on a perceptual loss. Ablation demonstrates the
contribution of both multi-reference and hierarchical attention-based sampling
to overall performance. Perceptual and quantitative ground-truth evaluation
demonstrates significant improvement in performance even when the reference
images deviate significantly from the target image. The project website can be
found at this https URL

本文提出了一种新颖的基于注意力的多参考超分辨率网络（AMRSR），该网络可以在保持空间相干性的同时，通过学习从多个参考图像中自适应地传输最相似的纹理到超分辨率输出来提高细节。与现有的参考超分辨率方法相比，多参考和分层注意力采样方法的结合实现了显着改进。