DBSAGAN: Dual Branch Split Attention Generative Adversarial Network for Super-Resolution Reconstruction in Remote Sensing Images
Author:
Affiliation:
1. Satellite Ground Systems Department, China Centre for Resources Satellite Data and Application, Beijing, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology
Link
http://xplorestaging.ieee.org/ielx7/8859/10034981/10102463.pdf?arnumber=10102463
Reference16 articles.
1. Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
2. Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-Resolution
3. Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
4. Focal frequency loss for image reconstruction and synthesis;jiang;arXiv 2012 12821,2020
5. A U-Net Based Discriminator for Generative Adversarial Networks
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1. Diffusion-Geo: A Two-Stage Controllable Text-To-Image Generative Model for Remote Sensing Scenarios;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
2. A Two-Branch Multiscale Residual Attention Network for Single Image Super-Resolution in Remote Sensing Imagery;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
3. A Random Medium Modeling Method Based on Wasserstein Convolutional Generative Adversarial Networks and Velocity Mask;IEEE Transactions on Geoscience and Remote Sensing;2024
4. Super-Resolution Reconstruction of Particleboard Images Based on Improved SRGAN;Forests;2023-09-10
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