Super-Resolution of Digital Elevation Model with Local Implicit Function Representation
Author:
Affiliation:
1. Northwestern Polytechnical University,School of Automation,Xi’an,P.R. China
2. FACRI,Xi’an,P.R. China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9942815/9943052/09943113.pdf?arnumber=9943113
Reference16 articles.
1. RSPCN: Super-Resolution of Digital Elevation Model Based on Recursive Sub-Pixel Convolutional Neural Networks
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3. Sape: Spatially-adaptive progressive encoding for neural optimization;hertz;Advances in neural information processing systems,2021
4. Image Quality Metrics: PSNR vs. SSIM
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1. High-Resolution Digital Bathymetric Model (DBM) With Uncertainty Reconstructed by Deep Learning With Dropout;IEEE Transactions on Geoscience and Remote Sensing;2024
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3. Master GAN: Multiple Attention is all you Need: A Multiple Attention Guided Super Resolution Network for Dems;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16
4. A Global-Information-Constrained Deep Learning Network for Digital Elevation Model Super-Resolution;Remote Sensing;2023-01-04
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