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.
IEEE Trans
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Presents a non-distortive cancelable face template method via targeted image distortion that maintains identity signals for neural embedding models on MNIST and LFW data.
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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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Embedding Non-Distortive Cancelable Face Template Generation
Presents a non-distortive cancelable face template method via targeted image distortion that maintains identity signals for neural embedding models on MNIST and LFW data.