Stochastic Flow Matching for Resolving Small-Scale Physics in Data-Limited Physical Sciences
Stochastic Flow Matching (SFM) effectively addresses the challenge of resolving small-scale physics in data-limited scenarios, particularly in atmospheric downscaling, by combining deterministic encoding of large-scale dynamics with stochastic flow matching in latent space.
Super-Resolution Algorithm Enhances EOS-06 OCM-3 Image Resolution for Various Earth Observation Applications
This research demonstrates the successful development and implementation of a deep learning-based super-resolution algorithm (SOCM-3) that significantly enhances the spatial resolution of images captured by the EOS-06 OCM-3 sensor, leading to improved clarity and detail in various Earth observation applications, including cryosphere, vegetation, and ocean monitoring.
Multi-Scale Implicit Transformer für beliebige Super-Resolution-Skalen
Multi-Scale Implicit Transformer verbessert die Super-Resolution durch Multi-Scale Merkmaleffekte.
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution
Die Einführung eines adaptiven Multi-Modalen Fusionssystems zur räumlich variablen Kernel-Verfeinerung mit Diffusionsmodell für die blinde Bild-Super-Resolution.
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