{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/darts-double-attention-reference-based","title":"DARTS: Double Attention Reference-based Transformer for Super-resolution","arxiv_id":"2307.08837","date":"2023-07-17","proceeding":null,"authors":["Masoomeh Aslahishahri","Jordan Ubbens","Ian Stavness"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2307.08837v1","url_pdf":"https://arxiv.org/pdf/2307.08837v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"darts-double-attention-reference-based","repo_url":"https://github.com/bia006/darts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-super-resolution","task_name":"Image Super-Resolution"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"ssim","task_name":"SSIM"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[{"method_slug":"darts","method_name":"DARTS"},{"method_slug":"low-resolution-input","method_name":"Low-resolution input"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2307.08837","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}