GSTurb: Gaussian Splatting for Atmospheric Turbulence Mitigation
Authors: Hanliang Du, Zhangji Lu, Zewei Cai, Qijian Tang, Qifeng Yu, Xiaoli Liu
Organizations: Faculty of Science and Technology, University of Macau, Macau 999078, China · College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518000, China · College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen, 518060, China · State Key Laboratory of Radio Frequency Heterogeneous Integration (Shenzhen University), Shenzhen University, Shenzhen, 518000, China · Shenzhen Key Laboratory of Intelligent Optical Measurement and Detection, Shenzhen University, Shenzhen, 518000, China · Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, Changsha, 410073, China · College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, 410073, China
Atmospheric turbulence causes significant image degradation due to pixel displacement (tilt) and blur, particularly in long-range imaging applications. In this paper, we propose a novel framework for atmospheric turbulence mitigation, GSTurb, which integrates optical flow-guided tilt correction and Gaussian splatting for modeling non-isoplanatic blur. The framework employs Gaussian parameters to represent tilt and blur, and optimizes them across multiple frames to enhance restoration. Experimental results on the ATSyn-static dataset demonstrate the effectiveness of our method, achieving a peak PSNR of 27.67 dB and SSIM of 0.8735. Compared to the state-of-the-art method, GSTurb improves PSNR by 1.3 dB (a 4.5% increase) and SSIM by 0.048 (a 5.8% increase). Additionally, on real datasets, including the TSRWGAN Real-World and CLEAR datasets, GSTurb outperforms existing methods, showing significant improvements in both qualitative and quantitative performance. These results highlight that combining optical flow-guided tilt correction with Gaussian splatting effectively enhances image restoration under both synthetic and real-world turbulence conditions. The code for this method will be available at https://github.com/DuhlLiamz/3DGS_turbulence/tree/main.
May 25, 2026·Zixiao Hu, Tianyu Li, Guoqing Wang +4Image Deblurring
University of Electronic Science and Technology of China · Beijing Institute of Space Mechanics and Electricity · School of Physics, Northwest University Xi’an
San Diego State University, Computational Science Research Center, Department of Mathematics and Statistics, 5500 Campanile Drive, San Diego, CA 92182, USA · San Diego State University, Department of Mathematics and Statistics, 5500 Campanile Drive, San Diego, CA 92182, USA