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洞見 - Turbulence Mitigation
Wavelet-Based Burst Accumulation Methods for Mitigating Turbulence in Image Sequences
This research paper introduces novel wavelet-based burst accumulation methods for restoring images degraded by atmospheric turbulence, demonstrating superior performance compared to Fourier-based techniques.
Efficient Atmospheric Turbulence Mitigation using Deep Learning with Physics-Grounded Modeling
The core message of this work is to develop an efficient deep learning-based method for mitigating atmospheric turbulence in images and videos by carefully integrating insights from classical turbulence mitigation algorithms and leveraging a physics-grounded data synthesis approach.
Unsupervised Implicit Neural Representations for General Turbulence Mitigation
NeRT, an unsupervised and physically grounded deep learning method, can effectively mitigate various types of turbulence, including atmospheric and water turbulence, without relying on domain-specific priors or large training datasets.
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