@inproceedings{
zhao2024efficient,
title={Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation},
author={Xiaole Zhao and Linze Li and Chengxing Xie and XIAOMING ZHANG and Ting Jiang and Wenjie Lin and Shuaicheng Liu and Tianrui Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=rE9i5h4xPl}
}
Transformer-based deep models for single image super-resolution (SISR) have greatly improved the performance of lightweight SISR
tasks in recent years. However, they often suffer from heavy computational burden and slow inference due to the complex calculation of multi-head self-attention (MSA), seriously hindering their practical application and deployment. In this work, we present an efficient SR
model to mitigate the dilemma between model efficiency and SR performance, which is dubbed Entropy Attention and Receptive Field
Augmentation network (EARFA), and composed of a novel entropy attention (EA) and a shifting large kernel attention (SLKA). From
the perspective of information theory, EA increases the entropy of intermediate features conditioned on a Gaussian distribution,
providing more informative input for subsequent reasoning. On the other hand, SLKA extends the receptive field of SR models with the
assistance of channel shifting, which also favors to boost the diversity of hierarchical features. Since the implementation of EA and
SLKA does not involve complex computations (such as extensive matrix multiplications), the proposed method can achieve faster
nonlinear inference than Transformer-based SR models while maintaining better SR performance. Extensive experiments show that
the proposed model can significantly reduce the delay of model inference while achieving the SR performance comparable with
other advanced models.
Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation
Xiaole Zhao Linze Li Chengxing Xie XIAOMING ZHANG Ting Jiang Wenjie Lin Shuaicheng Liu Tianrui Li