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Lightweight Single Image Super-Resolution via Efficient Mixture of Transformers and Convolutional Networks.

Luyang Xiao¹, Xiangyu Liao¹, Chao Ren¹

¹College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)

|August 29, 2024

Summary

We introduce the Local Global Union Network (LGUN), a novel AI model that merges Transformer and Convolutional Network strengths for efficient Single Image Super-Resolution (SISR). LGUN achieves superior performance on diverse datasets.

Keywords:

efficient global interaction fine-grained local modeling image super-resolution

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Area of Science:

Computer Vision
Artificial Intelligence
Deep Learning

Background:

Single Image Super-Resolution (SISR) is crucial for enhancing image quality.
Existing methods often struggle to balance local details and global context effectively.
Lightweight and high-performance networks are needed for practical SISR applications.

Purpose of the Study:

To propose a novel network architecture, the Local Global Union Network (LGUN), for efficient and high-performance Single Image Super-Resolution (SISR).
To leverage the complementary strengths of Transformers and Convolutional Networks in a unified framework.
To demonstrate the effectiveness of LGUN on both natural and satellite image datasets.

Main Methods:

The Local Global Union Network (LGUN) integrates Transformers for global context and Convolutional Networks for local features.
Multi-order Local Hierarchical Attention (MLHA) is used in shallow layers to encode local spatial information.
Dynamic Global Sparse Attention (DGSA) with Multi-stage Token Selection (MTS) is employed in deeper layers for global context modeling.

Main Results:

LGUN effectively combines global context interaction and local feature extraction.
The network demonstrates superior performance compared to existing SISR methods.
Experiments were conducted on both natural and satellite image datasets from optical and satellite sensors.

Conclusions:

LGUN offers a lightweight yet high-performance solution for Single Image Super-Resolution.
The proposed architecture successfully merges the benefits of Transformers and Convolutional Networks.
LGUN shows significant potential for various image super-resolution tasks across different sensor types.