Non-blind deblurring methods achieve decent performance under the accurate blur kernel assumption. Since the kernel uncertainty (i.e. kernel error) is inevitable in practice, semi-blind deblurring is suggested to handle it by introducing the prior of the kernel (or induced) error. However, how to design a suitable prior for the kernel (or induced) error remains challenging. Hand-crafted prior, incorporating domain knowledge, generally performs well but may lead to poor performance when kernel (or induced) error is complex. Data-driven prior, which excessively depends on the diversity and abundance of training data, is vulnerable to out-of-distribution blurs and images. To address this challenge, we suggest a dataset-free deep residual prior for the kernel induced error (termed as residual) expressed by a customized untrained deep neural network, which allows us to flexibly adapt to different blurs and images in real scenarios. By organically integrating the respective strengths of deep priors and hand-crafted priors, we propose an unsupervised semi-blind deblurring model which recovers the latent image from the blurry image and inaccurate blur kernel. To tackle the formulated model, an efficient alternating minimization algorithm is developed. Extensive experiments demonstrate the favorable performance of the proposed method as compared to data-driven and model-driven methods in terms of image quality and the robustness to the kernel error.

翻译：非盲去模糊方法在精确模糊核假设下性能表现良好。由于核不确定性（即核误差）在实际中不可避免，半盲去模糊通过引入核（或诱导）误差的先验来应对这一问题。然而，如何为核（或诱导）误差设计合适的先验仍然具有挑战性。融合领域知识的手工先验通常表现良好，但在核（或诱导）误差复杂时可能导致性能不佳。而数据驱动先验过度依赖训练数据的多样性与丰富性，对分布外模糊核和图像较为脆弱。为解决这一挑战，我们提出了一种无数据集的深度残差先验用于核诱导误差（称为残差），该先验由定制的未训练深度神经网络表示，使我们能够灵活适应真实场景中的不同模糊核和图像。通过有机整合深度先验与手工先验各自的优势，我们提出了一种无监督半盲去模糊模型，可从模糊图像和不精确模糊核中恢复潜在图像。针对所建立的模型，我们开发了一种高效的交替最小化算法。大量实验表明，与数据驱动和模型驱动方法相比，所提方法在图像质量和核误差鲁棒性方面均具有优越性能。