$$\hbox {S}^2\hbox {RGAN}$$: sonar-image super-resolution based on generative adversarial network
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Since 2014, generative adversarial training algorithms have been widely used in unimodal applications such as scene generation, image-to-image translation, and image super-resolution.
“Since their development in 2014, generative adversarial training algorithms have been widely used in various unimodal applications such as scene generation [17], image-to-image translation [18], and image super-resolution [224, 225].”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [224]
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References 36 · Cited by 23
- Authors
- Song, H., Wang, M., Zhang, L., Li, Y., Jiang, Z., & Yin, G.
- Affiliations
- Harbin Engineering University (CN)
- Corresponding author
- Zhang, Liguo
- Venue
- The Visual Computer
- Published
- 2020-10-14
- Volume
- 37
- Issue
- 8
- Pages
- 2285-2299
- ISSN
- 0178-2789 (ISSN-L)
- 1432-2315
- OpenAlex
- W3093308980
- Cited by
- 23
- References
- 36
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 3 · 2023: 3 · 2022: 6 · 2021: 3
- FWCI
- 1.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Visual Computer (publishedVersion)
- Funders
- National Natural Science Foundation of China (61501132)
- China Postdoctoral Science Foundation (2019M661319)
- Heilongjiang Postdoctoral Scientific Research Developmental Foundation (LBH-Q17042)
- Fundamental Research Funds for the Central Universities (3072019CFT0603)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- generative adversarial networks, underwater imaging, residual-in-residual dense blocks, transfer learning, perceptual loss, low-resolution images, underwater target recognition
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