Lightweight Single Image Super-Resolution via Efficient Mixture of Transformers and Convolutional Networks.
Sensors (Basel)
; 24(16)2024 Aug 06.
Article
en En
| MEDLINE
| ID: mdl-39204794
ABSTRACT
In this paper, we propose a Local Global Union Network (LGUN), which effectively combines the strengths of Transformers and Convolutional Networks to develop a lightweight and high-performance network suitable for Single Image Super-Resolution (SISR). Specifically, we make use of the advantages of Transformers to provide input-adaptation weighting and global context interaction. We also make use of the advantages of Convolutional Networks to include spatial inductive biases and local connectivity. In the shallow layer, the local spatial information is encoded by Multi-order Local Hierarchical Attention (MLHA). In the deeper layer, we utilize Dynamic Global Sparse Attention (DGSA), which is based on the Multi-stage Token Selection (MTS) strategy to model global context dependencies. Moreover, we also conduct extensive experiments on both natural and satellite datasets, acquired through optical and satellite sensors, respectively, demonstrating that LGUN outperforms existing methods.
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Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Sensors (Basel)
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Suiza