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Recursive Generalization Transformer for Image Super-Resolution
Feb. 26, 2024, 5:46 a.m. | Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xiaokang Yang
cs.CV updates on arXiv.org arxiv.org
Abstract: Transformer architectures have exhibited remarkable performance in image super-resolution (SR). Since the quadratic computational complexity of the self-attention (SA) in Transformer, existing methods tend to adopt SA in a local region to reduce overheads. However, the local design restricts the global context exploitation, which is crucial for accurate image reconstruction. In this work, we propose the Recursive Generalization Transformer (RGT) for image SR, which can capture global spatial information and is suitable for high-resolution images. …
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