https://publications.eai.eu/index.php/inis <p>EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is open access, a peer-reviewed scholarly journal focused on ubiquitous computing, cloud computing, and cyber-physical system, all kinds of networks in large-scale factories, including a lot of traditional and new industries. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). Authors are not charged for article submission and processing. This journal is co-organised, and managed by Duy Tan University, Vietnam.</p> European Alliance for Innovation (EAI) en-US EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2410-0218 <p>This is an open-access article distributed under the terms of the Creative Commons Attribution <a href="https://creativecommons.org/licenses/by/3.0/" target="_blank" rel="noopener">CC BY 3.0</a> license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.</p> https://publications.eai.eu/index.php/inis/article/view/2774 <p>With the advancement of deep learning, single-image super-resolution (SISR) has made significant strides. However, most current SISR methods are challenging to employ in real-world applications because they are doubtlessly employed by substantial computational and memory costs caused by complex operations. Furthermore, an efficient dataset is a key factor for bettering model training. The hybrid models of CNN and Vision Transformer can be more efficient in the SISR task. Nevertheless, they require substantial or extremely high-quality datasets for training that could be unavailable from time to time. To tackle these issues, a solution combined by applying a Lightweight Bimodal Network (LBNet) and Patch-Mosaic data augmentation method which is the enhancement of CutMix and YOCO is proposed in this research. With patch-oriented Mosaic data augmentation, an efficient Symmetric CNN is utilized for local feature extraction and coarse image restoration. Plus, a Recursive Transformer aids in fully grasping the long-term dependence of images, enabling the global information to be fully used to refine texture details. Extensive experiments have shown that LBNet with the proposed data augmentation with zero-free additional parameters method outperforms the original LBNet and other state-of-the-art techniques in which image-level data augmentation is applied.</p> Quoc Toan Nguyen Tang Quang Hieu Copyright (c) 2023 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://creativecommons.org/licenses/by/3.0/ 2023-05-25 2023-05-25 10 2 e1 e1 10.4108/eetinis.v10i2.2774