
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.


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wavelet-based-burst-accumulation-methods-for-mitigating-turbulence-in-image-sequences


Wavelet-Based Burst Accumulation Methods for Mitigating Turbulence in Image Sequences


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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.


efficient-atmospheric-turbulence-mitigation-using-deep-learning-with-physics-grounded-modeling


Efficient Atmospheric Turbulence Mitigation using Deep Learning with Physics-Grounded Modeling



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.


unsupervised-implicit-neural-representations-for-general-turbulence-mitigation


Unsupervised Implicit Neural Representations for General Turbulence Mitigation
