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：本文提出了一种新颖的基于深度学习的图像去模糊逆问题处理流程，通过利用合成数据增强与预训练技术实现突破。我们的成果源于近期举办的2021年赫尔辛基去模糊挑战赛（Heldel Debliur Challenge 2021）中提交的优胜方案，该赛事旨在探索真实数据场景下最先进去模糊算法的性能极限。该挑战任务要求对随机文本的离焦图像进行去模糊，并通过下游光学字符识别（OCR）评分函数最大化性能。我们方案的关键步骤是采用数据驱动方法估计描述模糊过程的物理前向模型，从而实时生成由清晰图像与模糊图像构成的数据流，显著扩充了挑战赛提供的少量标注数据。实际去模糊流程包含基于估算前向模型实现的径向镜头畸变近似逆变换，以及采用端到端训练的U型网络（U-Net）架构。我们的算法是唯一通过最高难度关卡的方法，字符识别准确率超过70%。该研究充分契合数据驱动机器学习的范式，并验证了其在逆问题中的有效性。除详细阐述方法论外，我们还在系列消融实验中分析了关键设计选择的重要性。参赛代码已开源至https://github.com/theophil-trippe/HDC_TUBerlin_version_1。