Imaging through the Atmosphere using Turbulence Mitigation Transformer

• URL: http://arxiv.org/abs/2207.06465v2
• Date: Mon, 11 Dec 2023 06:29:54 GMT
• Title: Imaging through the Atmosphere using Turbulence Mitigation Transformer
• Authors: Xingguang Zhang, Zhiyuan Mao, Nicholas Chimitt, Stanley H. Chan
• Abstract summary: Restoring images distorted by atmospheric turbulence is a ubiquitous problem in long-range imaging applications. Existing deep-learning-based methods have demonstrated promising results in specific testing conditions. We introduce the turbulence mitigation transformer (TMT) that explicitly addresses these issues.
• Score: 15.56320865332645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Restoring images distorted by atmospheric turbulence is a ubiquitous problem in long-range imaging applications. While existing deep-learning-based methods have demonstrated promising results in specific testing conditions, they suffer from three limitations: (1) lack of generalization capability from synthetic training data to real turbulence data; (2) failure to scale, hence causing memory and speed challenges when extending the idea to a large number of frames; (3) lack of a fast and accurate simulator to generate data for training neural networks. In this paper, we introduce the turbulence mitigation transformer (TMT) that explicitly addresses these issues. TMT brings three contributions: Firstly, TMT explicitly uses turbulence physics by decoupling the turbulence degradation and introducing a multi-scale loss for removing distortion, thus improving effectiveness. Secondly, TMT presents a new attention module along the temporal axis to extract extra features efficiently, thus improving memory and speed. Thirdly, TMT introduces a new simulator based on the Fourier sampler, temporal correlation, and flexible kernel size, thus improving our capability to synthesize better training data. TMT outperforms state-of-the-art video restoration models, especially in generalizing from synthetic to real turbulence data. Code, videos, and datasets are available at \href{https://xg416.github.io/TMT}{https://xg416.github.io/TMT}.

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