Abstract
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 this https URL.
Abstract (translated)
大气湍流会导致图像退化，主要原因是像素位移（倾斜）和模糊，尤其是在长距离成像应用中。本文提出了一种新的大气湍流缓解框架GSTurb，该框架结合了光学流引导的倾斜校正和高斯喷射来建模非同像平面模糊。此框架使用高斯参数表示倾斜和模糊，并在多帧之间优化这些参数以增强图像恢复效果。 实验结果表明，在ATSyn-static数据集上，我们的方法达到了峰值PSNR为27.67 dB和SSIM为0.8735的性能。相较于最先进的方法，GSTurb将PSNR提高了1.3 dB（增幅4.5%），并将SSIM提高了0.048（增幅5.8%）。此外，在真实数据集TSRWGAN Real-World和CLEAR上，GSTurb也优于现有方法，无论是在定性还是定量性能方面都表现出显著的改进。这些结果表明，结合光学流引导的倾斜校正与高斯喷射可以有效地增强合成湍流和现实世界湍流条件下的图像恢复。 该方法的相关代码将在此网址提供：[此链接](https://this-url.com)（请将此处的链接替换为实际提供的GitHub或其他存储库链接）。
URL
https://arxiv.org/abs/2602.22800
