Paper Info
Title
Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and a New Physics-Inspired Transformer Model.
Abstract
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. The code and datasets are available at github.com/VITA-Group/TurbNet.
Year
DOI
Venue
2022
10.1007/978-3-031-19800-7_25
European Conference on Computer Vision
Keywords
DocType
Citations 
Atmospheric turbulence mitigation,Image restoration
Conference
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Zhiyuan Mao100.68
Ajay Jaiswal200.34
Zhangyang Wang343775.27
Stanley H. Chan440330.95