Although the distortion correction of fisheye images has been extensively studied, the correction of fisheye videos is still an elusive challenge. For different frames of the fisheye video, the existing image correction methods ignore the correlation of sequences, resulting in temporal jitter in the corrected video. To solve this problem, we propose a temporal weighting scheme to get a plausible global optical flow, which mitigates the jitter effect by progressively reducing the weight of frames. Subsequently, we observe that the inter-frame optical flow of the video is facilitated to perceive the local spatial deformation of the fisheye video. Therefore, we derive the spatial deformation through the flows of fisheye and distorted-free videos, thereby enhancing the local accuracy of the predicted result. However, the independent correction for each frame disrupts the temporal correlation. Due to the property of fisheye video, a distorted moving object may be able to find its distorted-free pattern at another moment. To this end, a temporal deformation aggregator is designed to reconstruct the deformation correlation between frames and provide a reliable global feature. Our method achieves an end-to-end correction and demonstrates superiority in correction quality and stability compared with the SOTA correction methods.

翻译：尽管鱼眼图像的畸变矫正已得到广泛研究，但鱼眼视频的矫正仍是一个难以攻克的挑战。现有图像矫正方法忽略了鱼眼视频不同帧之间的序列相关性，导致矫正后视频出现时间抖动。为解决此问题，我们提出一种时间加权方案以获取合理的全局光流，通过逐步降低帧权重来缓解抖动效应。随后，我们观察到视频的帧间光流有助于感知鱼眼视频的局部空间形变，因此通过鱼眼视频与无畸变视频的光流推导空间形变，从而提升预测结果的局部精度。然而，逐帧独立矫正会破坏时间相关性。基于鱼眼视频的特性，畸变运动物体可能在另一时刻找到其无畸变模式。为此，我们设计了时间形变聚合器，用于重建帧间形变相关性并提供可靠的全局特征。该方法实现了端到端矫正，并在矫正质量与稳定性方面优于现有最优矫正方法。