D-SHIFT uses generative adversarial networks to transfer high spatial resolution from monthly GRACE mascon TWSA products to daily fields, reporting 2.3 cm global RMSE and improved basin trends.
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Flow matching achieves single-step pixel accuracy and 20-step perceptual quality for Sentinel-2 super-resolution, outperforming diffusion and Real-ESRGAN while enabling large-scale 2.5 m land-cover products.
NeuroGAN-3D is a 3D GAN model that super-resolves volumetric rs-fMRI spatial maps and outperforms a conventional baseline.
citing papers explorer
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D-SHIFT: Transferring High Spatial Information from GRACE Monthly TWSA Mascon to Daily Products Using Generative Adversarial Networks
D-SHIFT uses generative adversarial networks to transfer high spatial resolution from monthly GRACE mascon TWSA products to daily fields, reporting 2.3 cm global RMSE and improved basin trends.
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Flow matching for Sentinel-2 super-resolution: implementation, application, and implications
Flow matching achieves single-step pixel accuracy and 20-step perceptual quality for Sentinel-2 super-resolution, outperforming diffusion and Real-ESRGAN while enabling large-scale 2.5 m land-cover products.
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NeuroGAN-3D: Enhancing Intrinsic Functional Brain Networks via High-Fidelity 3D Generative Super-Resolution
NeuroGAN-3D is a 3D GAN model that super-resolves volumetric rs-fMRI spatial maps and outperforms a conventional baseline.