论文标题
单帧大气湍流缓解：基准研究和新的物理启发的变压器模型
Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model
论文作者
论文摘要
已知大气湍流的图像恢复算法对设计的挑战性比模糊或噪声等传统更具挑战性，因为湍流引起的失真是空间变化的模糊，几何变形和传感器噪声的纠缠。现有的基于CNN的恢复方法建立在具有静态重量的卷积内核上的不足以处理空间动态的大气湍流效果。为了解决这个问题，在本文中，我们提出了一个由物理启发的变压器模型，用于通过大气湍流进行成像。提出的网络利用变压器块的功率共同提取动态湍流失真图并恢复无湍流图像。此外，我们认识到缺乏全面的数据集，我们收集并介绍了两个新的现实世界中的湍流数据集，这些数据集允许使用经典目标指标（例如PSNR和SSIM）进行评估，并使用文本识别精度进行了新的任务驱动指标。实际测试集和所有相关代码都将公开可用。
Image restoration algorithms for atmospheric turbulence are known to be much more challenging to design than traditional ones such as blur or noise because the distortion caused by the turbulence is an entanglement of spatially varying blur, geometric distortion, and sensor noise. Existing CNN-based restoration methods built upon convolutional kernels with static weights are insufficient to handle the spatially dynamical atmospheric turbulence effect. To address this problem, in this paper, we propose a physics-inspired transformer model for imaging through atmospheric turbulence. The proposed network utilizes the power of transformer blocks to jointly extract a dynamical turbulence distortion map and restore a turbulence-free image. In addition, recognizing the lack of a comprehensive dataset, we collect and present two new real-world turbulence datasets that allow for evaluation with both classical objective metrics (e.g., PSNR and SSIM) and a new task-driven metric using text recognition accuracy. Both real testing sets and all related code will be made publicly available.
