We describe a method for recovering the irradiance underlying a collection of images corrupted by atmospheric turbulence. Since supervised data is often technically impossible to obtain, assumptions and biases have to be imposed to solve this inverse problem, and we choose to model them explicitly. Rather than initializing a latent irradiance ("template") by heuristics to estimate deformation, we select one of the images as a reference, and model the deformation in this image by the aggregation of the optical flow from it to other images, exploiting a prior imposed by Central Limit Theorem. Then with a novel flow inversion module, the model registers each image TO the template but WITHOUT the template, avoiding artifacts related to poor template initialization. To illustrate the robustness of the method, we simply (i) select the first frame as the reference and (ii) use the simplest optical flow to estimate the warpings, yet the improvement in registration is decisive in the final reconstruction, as we achieve state-of-the-art performance despite its simplicity. The method establishes a strong baseline that can be further improved by integrating it seamlessly into more sophisticated pipelines, or with domain-specific methods if so desired.

翻译：我们描述了一种从受大气湍流污染的图像集合中恢复辐照度的方法。由于监督数据在技术上往往无法获取，必须引入假设与偏差来解决这一逆问题，我们选择对其进行显式建模。该方法并非通过启发式初始化潜在辐照度（"模板"）来估计形变，而是选取其中一幅图像作为参考，利用中心极限定理施加的先验，通过聚合从该图像到其他图像的光流来建模参考图像自身的形变。随后借助新颖的流反向模块，模型无需模板即可将每幅图像配准至该模板，从而避免了因模板初始化不佳而产生的伪影。为验证方法的鲁棒性，我们仅需（i）选取首帧作为参考，（ii）使用最简单的光流估计扭曲，尽管方法简洁，但配准效果的显著提升确保了最终重建性能达到当前最优水平。该方法建立了强基线，可无缝集成至更复杂的流程中，或按需结合领域特定方法进行进一步改进。