Blind image deblurring (BID) has been extensively studied in computer vision and adjacent fields. Modern methods for BID can be grouped into two categories: single-instance methods that deal with individual instances using statistical inference and numerical optimization, and data-driven methods that train deep-learning models to deblur future instances directly. Data-driven methods can be free from the difficulty in deriving accurate blur models, but are fundamentally limited by the diversity and quality of the training data -- collecting sufficiently expressive and realistic training data is a standing challenge. In this paper, we focus on single-instance methods that remain competitive and indispensable. However, most such methods do not prescribe how to deal with unknown kernel size and substantial noise, precluding practical deployment. Indeed, we show that several state-of-the-art (SOTA) single-instance methods are unstable when the kernel size is overspecified, and/or the noise level is high. On the positive side, we propose a practical BID method that is stable against both, the first of its kind. Our method builds on the recent ideas of solving inverse problems by integrating the physical models and structured deep neural networks, without extra training data. We introduce several crucial modifications to achieve the desired stability. Extensive empirical tests on standard synthetic datasets, as well as real-world NTIRE2020 and RealBlur datasets, show the superior effectiveness and practicality of our BID method compared to SOTA single-instance as well as data-driven methods. The code of our method is available at: \url{https://github.com/sun-umn/Blind-Image-Deblurring}.

翻译：盲图像去模糊（BID）在计算机视觉及相关领域已被广泛研究。现代BID方法可分为两类：基于统计推理与数值优化的单实例处理方法，以及通过训练深度学习模型直接去模糊未来图像的数据驱动方法。数据驱动方法无需推导精确模糊模型，但其性能受限于训练数据的多样性与质量——收集足够表达力与真实性的训练数据仍是长期挑战。本文聚焦于仍具竞争力的单实例方法，但现有方法大多未明确处理未知核尺寸与强噪声的策略，阻碍实际部署。事实上，研究表明，当核尺寸过估计或噪声水平较高时，多种最先进（SOTA）单实例方法会出现不稳定现象。为此，我们提出首个对两者均保持稳定的实用BID方法。该方法基于融合物理模型与结构化深度神经网络求解逆问题的新范式，无需额外训练数据，并通过关键改进实现预期稳定性。在标准合成数据集以及真实场景的NTIRE2020与RealBlur数据集上的广泛实验表明，相较于SOTA单实例及数据驱动方法，本方法在有效性与实用性上均表现更优。方法代码见：\url{https://github.com/sun-umn/Blind-Image-Deblurring}。