Abstract
Restoring images distorted by atmospheric turbulence is a long-standing problem due to the spatially varying nature of the distortion, nonlinearity of the image formation process, and scarcity of training and testing data. Existing methods often have strong statistical assumptions on the distortion model which in many cases will lead to a limited performance in real-world scenarios as they do not generalize. To overcome the challenge, this paper presents an end-to-end physics-driven approach that is efficient and can generalize to real-world turbulence. On the data synthesis front, we significantly increase the image resolution that can be handled by the SOTA turbulence simulator by approximating the random field via wide-sense stationarity. The new data synthesis process enables the generation of large-scale multi-level turbulence and ground truth pairs for training. On the network design front, we propose the turbulence mitigation transformer (TMT), a two stage U-Net shaped multi-frame restoration network which has a noval efficient self-attention mechanism named temporal channel joint attention (TCJA). We also introduce a new training scheme that is enabled by the new simulator, and we design new transformer units to reduce the memory consumption. Experimental results on both static and dynamic scenes are promising, including various real turbulence scenarios.
Abstract (translated)
URL
https://arxiv.org/abs/2207.06465
