MHGAN: A Multi-Headed Generative Adversarial Network for Underwater Sonar Image Super-Resolution
Author:
Affiliation:
1. Department of Electronic Engineering, School of Information Science and Engineering, Fudan University, Shanghai, China
2. Shanghai Acoustic Laboratory, Chinese Academy of Sciences, Shanghai, China
Funder
National Natural Science Foundation of China
Shanghai Sailing Program
Shanghai Science and Technology Committee
Young Potential Program of Shanghai Acoustics Laboratory, Chinese Academy of Sciences
National Key Research and Development Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/10006360/10292661.pdf?arnumber=10292661
Reference68 articles.
1. Learning low-level vision
2. Multiattention Generative Adversarial Network for Remote Sensing Image Super-Resolution
3. Statistical and Structural Information Backed Full-Reference Quality Measure of Compressed Sonar Images
4. Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution
5. TDPN: Texture and Detail-Preserving Network for Single Image Super-Resolution
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1. Multibeam water column image super-resolution by combining morphological and intensity features;International Journal of Digital Earth;2024-09-02
2. Various Degradation: Dual Cross-Refinement Transformer for Blind Sonar Image Super-Resolution;IEEE Transactions on Geoscience and Remote Sensing;2024
3. Sonar Digital Twin Layer via Multiattention Networks With Feature Transfer;IEEE Transactions on Geoscience and Remote Sensing;2024
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