论文标题
评估数据集在运动脱张方法概括为真实图像中的作用
Assessing the Role of Datasets in the Generalization of Motion Deblurring Methods to Real Images
论文作者
论文摘要
成功地训练端到端的深层网络进行真实的运动去除，需要清晰/模糊的图像对数据集，这些图像对现实且多样化，足以实现概括以实现真实的图像。获得此类数据集仍然是一项具有挑战性的任务。在本文中，我们首先回顾了现有的DeBlurring基准数据集的局限性，并分析了DeBlurring网络缺乏对野外模糊图像的概括的根本原因。基于此分析，我们提出了一种有效的程序方法，以基于简单但有效的模型生成清晰/模糊的图像对。这允许生成几乎无限的多样化训练对，模仿逼真的模糊特性。我们通过在模拟对上训练现有的DeBlurring架构并在三个真实模糊图像的三个标准数据集上进行跨数据库评估来证明所提出的数据集的有效性。在使用提出的方法训练时，我们观察到了卓越的概括性能，以使动态场景的真实运动毛刺照片去除最终任务。
Successfully training end-to-end deep networks for real motion deblurring requires datasets of sharp/blurred image pairs that are realistic and diverse enough to achieve generalization to real blurred images. Obtaining such datasets remains a challenging task. In this paper, we first review the limitations of existing deblurring benchmark datasets and analyze the underlying causes for deblurring networks' lack of generalization to blurry images in the wild. Based on this analysis, we propose an efficient procedural methodology to generate sharp/blurred image pairs based on a simple yet effective model. This allows for generating virtually unlimited diverse training pairs mimicking realistic blur properties. We demonstrate the effectiveness of the proposed dataset by training existing deblurring architectures on the simulated pairs and performing cross-dataset evaluation on three standard datasets of real blurred images. When training with the proposed method, we observed superior generalization performance for the ultimate task of deblurring real motion-blurred photos of dynamic scenes.
