  We present DARTS, a transformer model for reference-based image
super-resolution. DARTS learns joint representations of two image distributions
to enhance the content of low-resolution input images through matching
correspondences learned from high-resolution reference images. Current
state-of-the-art techniques in reference-based image super-resolution are based
on a multi-network, multi-stage architecture. In this work, we adapt the double
attention block from the GAN literature, processing the two visual streams
separately and combining self-attention and cross-attention blocks through a
gating attention strategy. Our work demonstrates how the attention mechanism
can be adapted for the particular requirements of reference-based image
super-resolution, significantly simplifying the architecture and training
pipeline. We show that our transformer-based model performs competitively with
state-of-the-art models, while maintaining a simpler overall architecture and
training process. In particular, we obtain state-of-the-art on the SUN80
dataset, with a PSNR/SSIM of 29.83 / .809. These results show that attention
alone is sufficient for the RSR task, without multiple purpose-built
subnetworks, knowledge distillation, or multi-stage training.
