An Amalgamated CNN-Transformer Network for Lightweight Image Super-Resolution

被引：0

作者：

Fang, Jinsheng ^{[1
,2
]}

Lin, Hanjiang ^{[1
,2
]}

Zeng, Kun ^{[3
]}

机构：

[1] School of Computer Science and Engineering Minnan Normal University, Zhangzhou,363000, China

[2] Key Laboratory of Data Science and Intelligence Application Fujian Province University, Fujian, Zhangzhou,363000, China

[3] School of Computer and Control engineering Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University, Fuzhou,350108, China

来源：

Journal of Network Intelligence | 2024年 / 9卷 / 03期

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摘要：

Recently, Transformer-based methods for single image super-resolution (SISR) have achieved better performance advantages than the methods based on convolutional neural network (CNN). Exploiting self-attention mechanism to model global context definitely improves the SR results. However, the neglect of local information will bring inevitable reduction of the network performance. In this work, we propose an Amalgamated CNN-Transformer network for lightweight SR, namely ACTSR. Specifically, an amalgamated CNN-Transformer block (ACTB) is developed to extract the useful information of both local and global features. By employing stacked ACTBs, our ACTSR extracts more informative deep features beneficially for super-resolution reconstruction to improve network performance while keeps lightweight and flexible enough. Extensive experiments on commonly used benchmark datasets validate our ACTSR outperforms the advanced competitors. Our codes are available at: https://github.com/ginsengf/ACTSR. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

引用

页码：1376 / 1387