Lightweight Image Super-Resolution with Expectation-Maximization Attention Mechanism.pdf (2.3 MB)
Lightweight image super-resolution with expectation-maximization attention mechanism
journal contribution
posted on 2021-05-04, 11:07 authored by Xiangyuan Zhu, Kehua Guo, Sheng Ren, Bin Hu, Min Hu, Hui FangHui FangIn recent years, with the rapid development of deep learning, super-resolution methods based on convolutional neural networks (CNNs) have made great progress. However, the parameters and the required consumption of computing resources of these methods are also increasing to the point that such methods are difficult to implement on devices with low computing power. To address this issue, we propose a lightweight single image super-resolution network with an expectation-maximization attention mechanism (EMASRN) for better balancing performance and applicability. Specifically, a progressive multi-scale feature extraction block (PMSFE) is proposed to extract feature maps of different sizes. Furthermore, we propose an HR-size expectation-maximization attention block (HREMAB) that directly captures the long-range dependencies of HR-size feature maps. We also utilize a feedback network to feed the high-level features of each generation into the next generation’s shallow network. Compared with the existing lightweight single image super-resolution (SISR) methods, our EMASRN reduces the number of parameters by almost one-third. The experimental results demonstrate the superiority of our EMASRN over state-of-the-art lightweight SISR methods in terms of both quantitative metrics and visual quality. The source code can be downloaded at https://github.com/xyzhu1/EMASRN.
Funding
Natural Science Foundation of China under Grant 62076255
Hunan Provincial Science and Technology Plan Project 2020SK2059
National Science Foundation of Hunan Province, China, under Grant 2019JJ20025 and Grant 2019JJ40406
National Social Science Fund of China (No. 20&ZD120)
History
School
- Science
Department
- Computer Science
Published in
IEEE Transactions on Circuits and Systems for Video TechnologyVolume
32Issue
3Pages
1273 - 1284Publisher
Institute of Electrical and Electronics EngineersVersion
- AM (Accepted Manuscript)
Rights holder
© IEEEPublisher statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Acceptance date
2021-04-30Publication date
2021-05-10Copyright date
2021ISSN
1051-8215eISSN
1558-2205Publisher version
Language
- en