AI & Machine Learning

Continuous Exposure-Time Modeling for Realistic Atmospheric Turbulence Synthesis

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Researchers introduced ET-MTF, a model that accurately simulates atmospheric turbulence effects by considering exposure time as a continuous variable, leading to more realistic blur synthesis. This approach enhances the quality and generalization of models trained on synthetic data, crucial for improving long-range imaging systems and high-level vision tasks.

Read the full article at arXiv cs.CV (Vision)

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