Bibliographic Details
| Title: |
Guided Dual Networks for Single Image Super-Resolution |
| Authors: |
Wenhui Chen, Chuangchuang Liu, Yitong Yan, Longcun Jin, Xianfang Sun, Xinyi Peng |
| Source: |
IEEE Access, Vol 8, Pp 93608-93620 (2020) |
| Publisher Information: |
IEEE, 2020. |
| Publication Year: |
2020 |
| Collection: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| Subject Terms: |
Convolutional neural network, dual network, generative adversarial network, single image super-resolution, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| More Details: |
The PSNR-oriented super-resolution (SR) methods pursue high reconstruction accuracy, but tend to produce over-smoothed results and lose plenty of high-frequency details. The GAN-based SR methods aim to generate more photo-realistic images, but the hallucinatory details are often accompanied with unsatisfying artifacts and noise. To address these problems, we propose a guided dual super-resolution network (GDSR), which exploits the advantages of both the PSNR-oriented and the GAN-based methods to achieve a good trade-off between reconstruction accuracy and perceptual quality. Specifically, our network contains two branches, where one is trained to extract global information and the other to focus on detail information. In this way, our network simultaneously generates SR images with high accuracy and satisfactory visual quality. To obtain more high-frequency features, we use the global features extracted from the low-frequency branch to guide the training of the high-frequency branch. Besides, our method utilizes a mask network to adaptively recover the final super-resolved image. Extensive experiments on several standard benchmarks show that our proposed method achieves better performance compared with state-of-the-art methods. The source code and the results of our GDSR are available at https://github.com/wenchen4321/GDSR. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/9097227/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2020.2995175 |
| Access URL: |
https://doaj.org/article/c263d7417a5148b09af1cb847ab2a590 |
| Accession Number: |
edsdoj.263d7417a5148b09af1cb847ab2a590 |
| Database: |
Directory of Open Access Journals |
