In most of computer vision applications, motion blur is regarded as an undesirable artifact. However, it has been shown that motion blur in an image may have practical interests in fundamental computer vision problems. In this work, we propose a novel framework to estimate optical flow from a single motion-blurred image in an end-to-end manner. We design our network with transformer networks to learn globally and locally varying motions from encoded features of a motion-blurred input, and decode left and right frame features without explicit frame supervision. A flow estimator network is then used to estimate optical flow from the decoded features in a coarse-to-fine manner. We qualitatively and quantitatively evaluate our model through a large set of experiments on synthetic and real motion-blur datasets. We also provide in-depth analysis of our model in connection with related approaches to highlight the effectiveness and favorability of our approach. Furthermore, we showcase the applicability of the flow estimated by our method on deblurring and moving object segmentation tasks.

中文翻译：

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从单个运动模糊图像估计光流

在大多数计算机视觉应用中，运动模糊被认为是不希望的伪影。但是，已经显示出图像中的运动模糊可能对基本的计算机视觉问题有实际的兴趣。在这项工作中，我们提出了一种新颖的框架来以端到端的方式从单个运动模糊图像估计光流。我们使用变压器网络设计网络，以从运动模糊输入的编码特征中学习全局和局部变化的运动，并在无需显式框架监督的情况下解码左右框架特征。然后，使用流量估算器网络以粗到精的方式从解码后的特征中估算光流量。通过对合成运动模糊数据集和真实运动模糊数据集进行的大量实验，我们对模型进行了定性和定量评估。我们还将结合相关方法对我们的模型进行深入分析，以突出我们方法的有效性和可取性。此外，我们展示了通过我们的方法估算的流程在去模糊和移动对象分割任务上的适用性。