DARTS: Double Attention Reference-based Transformer for Super-resolution

• URL: http://arxiv.org/abs/2307.08837v1
• Date: Mon, 17 Jul 2023 20:57:16 GMT
• Title: DARTS: Double Attention Reference-based Transformer for Super-resolution
• Authors: Masoomeh Aslahishahri, Jordan Ubbens, Ian Stavness
• Abstract summary: We present DARTS, a transformer model for reference-based image super-resolution. DARS learns joint representations of two image distributions to enhance the content of low-resolution input images. We show that our transformer-based model performs competitively with state-of-the-art models.
• Score: 12.424350934766704
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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