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A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution.

Yonggui Zhu¹, Guofang Li²

¹School of Data Science and Intelligent Media, Communication University of China, Beijing 100024, China.

Sensors (Basel, Switzerland)

|October 28, 2023

Summary

This study introduces a lightweight recurrent grouping attention network for video super-resolution. The novel model efficiently aggregates temporal information, achieving state-of-the-art performance with significantly reduced parameters.

Keywords:

attention supplementation feature reconstruction temporal grouping attention video super-resolution

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Area of Science:

Computer Vision
Artificial Intelligence

Background:

Video super-resolution (VSR) models require effective temporal information aggregation.
Current VSR models often have large parameter counts, increasing hardware demands.

Purpose of the Study:

To propose a novel, lightweight recurrent grouping attention network for VSR.
To reduce the computational burden and parameter size of VSR models.

Main Methods:

Designed forward and backward feature extraction modules for bidirectional temporal information collection.
Introduced a grouping mechanism for efficient spatio-temporal information gathering.
Utilized an attention supplementation module to expand the information gathering range.

Main Results:

The proposed model has only 0.878 M parameters, significantly lower than mainstream VSR models.
Achieved state-of-the-art performance on multiple benchmark datasets.
Demonstrated effective aggregation of temporal and spatio-temporal information.

Conclusions:

The lightweight recurrent grouping attention network offers a computationally efficient solution for VSR.
The model's architecture effectively captures necessary spatio-temporal details for high-resolution video reconstruction.
This approach balances performance with reduced resource requirements.