Image super-resolution using dilated neighborhood attention transformer
Li Chen,
Jinnian Zuo,
Kai Du
et al.
Abstract:Transformer-based methods have achieved impressive performance in image super-resolution (SR). To reduce the computational cost and redundancy of global attention, most transformer-based methods adopt a localized attention mechanism, which diminishes the desirable characteristics of self-attention (SA), such as the effective modeling of long-range dependencies and the ability to capture a global receptive field. To alleviate this problem, we propose a dilated neighborhood attention transformer for image SR (Di… Show more
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