SCAT: Shift Channel Attention Transformer for Remote Sensing Image Super-Resolution

The quadratic increase in computational complexity caused by global receptive fields has been a persistent challenge when applying Transformer-based methods in remote sensing image super-resolution (RSISR), involving high-resolution images. Channel attention (CA)-based Transformers offer an efficien...

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Hlavní autoři:	Yingdong Kang, Xuemin Zhang, Shaoju Wang, Guang Jin
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2025-01-01
Edice:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Témata:	Deep learning optical remote sensing super-resolution (SR) transformers
On-line přístup:	https://ieeexplore.ieee.org/document/10886926/
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