综述深度学习在消除大气湍流导致的图像扭曲方面的进展。
Deep Learning Techniques for Atmospheric Turbulence Removal: A Review
- 对比Transformer、Swin、Mamba等模型在时空图像去畸变中的表现
- 指出深度学习比传统方法更快，更适合部署在小型设备上
- 适合从事图像复原、遥感成像与AI硬件部署的研究者
大气湍流对获取的影像造成影响，使图像解读和场景分析极为困难，降低了传统分类与目标追踪方法的有效性。恢复受大气湍流扭曲的场景图像是一项挑战性任务。该效应由随机的空间变化扰动引起，使得传统的基于模型的方法因复杂度高、内存需求大而难以实施。深度学习方法则具备运算速度快、可在小型设备上实现的优势。本文综述了大气湍流的特性及其对影像的影响，并对比了当前最先进的深度神经网络（包括Transformer、Swin和Mamba）在缓解时空图像失真方面的性能表现。该综述为如何结合数据集、评估指标以及现有与新开发的深度学习方法，推动下一代湍流抑制技术的发展提供了路线图。
原文摘要 · Abstract (English)
The influence of atmospheric turbulence on acquired imagery makes image interpretation and scene analysis extremely difficult and reduces the effectiveness of conventional approaches for classifying and tracking objects of interest in the scene. Restoring a scene distorted by atmospheric turbulence is also a challenging problem. The effect, which is caused by random, spatially varying perturbations, makes conventional model-based approaches difficult and, in most cases, impractical due to complexity and memory requirements. Deep learning approaches offer faster operation and are capable of implementation on small devices. This paper reviews the characteristics of atmospheric turbulence and its impact on acquired imagery. It compares the performance of various state-of-the-art deep neural networks, including Transformers, SWIN and Mamba, when used to mitigate spatio-temporal image distortions.
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