This work presents a novel deep-learning-based pipeline for the inverse problem of image deblurring, leveraging augmentation and pre-training with synthetic data. Our results build on our winning submission to the recent Helsinki Deblur Challenge 2021, whose goal was to explore the limits of state-of-the-art deblurring algorithms in a real-world data setting. The task of the challenge was to deblur out-of-focus images of random text, thereby in a downstream task, maximizing an optical-character-recognition-based score function. A key step of our solution is the data-driven estimation of the physical forward model describing the blur process. This enables a stream of synthetic data, generating pairs of ground-truth and blurry images on-the-fly, which is used for an extensive augmentation of the small amount of challenge data provided. The actual deblurring pipeline consists of an approximate inversion of the radial lens distortion (determined by the estimated forward model) and a U-Net architecture, which is trained end-to-end. Our algorithm was the only one passing the hardest challenge level, achieving over 70% character recognition accuracy. Our findings are well in line with the paradigm of data-centric machine learning, and we demonstrate its effectiveness in the context of inverse problems. Apart from a detailed presentation of our methodology, we also analyze the importance of several design choices in a series of ablation studies. The code of our challenge submission is available under https://github.com/theophil-trippe/HDC_TUBerlin_version_1.
翻译:这项工作展示了一个新的深层次的、基于深层次的管道,用于应对图像变形、利用合成数据进行放大和预培训的反面问题。我们的成果基于我们向最近的赫尔辛基Deblur 挑战 2021 提交的成功数据, 其目的是探索现实世界数据设置中最先进的变形算法的局限性。 挑战的任务是在下游任务中将随机文本的图像除色, 从而最大限度地实现光学字符识别基于合成数据的评分功能。 我们解决方案的一个关键步骤是对描述提交过程模糊的物理前方模型进行数据驱动的估算。 这将使合成数据流流流流流能够生成一对地真真和模糊的实时图像, 用于广泛增加所提供的少量挑战数据。 实际变色管道的任务是将光学扭曲的图像除色外外, 从而在下游任务中, 最大限度地增加光学选择基于光学特征的评分值。 我们的算法只是经过最严峻的前期模型, 在模型中,我们从一个最严峻的挑战级别上, 在模型中,我们从一个最精确的模型中, 也展示了70 %的系统分析方法。 我们的模型中, 正确理解了我们的一些定义中, 我们的排序中, 正确的方法也展示了。