Motion deblurring is one of the fundamental problems of computer vision and has received continuous attention. The variability in blur, both within and across images, imposes limitations on non-blind deblurring techniques that rely on estimating the blur kernel. As a response, blind motion deblurring has emerged, aiming to restore clear and detailed images without prior knowledge of the blur type, fueled by the advancements in deep learning methodologies. Despite strides in this field, a comprehensive synthesis of recent progress in deep learning-based blind motion deblurring is notably absent. This paper fills that gap by providing an exhaustive overview of the role of deep learning in blind motion deblurring, encompassing datasets, evaluation metrics, and methods developed over the last six years. Specifically, we first introduce the types of motion blur and the fundamental principles of deblurring. Next, we outline the shortcomings of traditional non-blind deblurring algorithms, emphasizing the advantages of employing deep learning techniques for deblurring tasks. Following this, we categorize and summarize existing blind motion deblurring methods based on different backbone networks, including convolutional neural networks, generative adversarial networks, recurrent neural networks, and Transformer networks. Subsequently, we elaborate not only on the fundamental principles of these different categories but also provide a comprehensive summary and comparison of their advantages and limitations. Qualitative and quantitative experimental results conducted on four widely used datasets further compare the performance of SOTA methods. Finally, an analysis of present challenges and future pathways. All collected models, benchmark datasets, source code links, and codes for evaluation have been made publicly available at https://github.com/VisionVerse/Blind-Motion-Deblurring-Survey

翻译：运动去模糊是计算机视觉的基本问题之一，一直受到持续关注。模糊在图像内部及图像之间的变异性，使得依赖模糊核估计的非盲去模糊技术存在局限性。为此，基于深度学习方法的发展，盲运动去模糊应运而生，旨在无需预知模糊类型的情况下恢复清晰细节的图像。尽管该领域取得了进展，但对近年来基于深度学习的盲运动去模糊研究的全面综述仍明显缺失。本文通过全面概述深度学习在盲运动去模糊中的作用填补了这一空白，涵盖了近六年来开发的数据集、评估指标和方法。具体而言，我们首先介绍运动模糊的类型及去模糊的基本原理。接着，概述传统非盲去模糊算法的不足，强调采用深度学习技术进行去模糊任务的优势。随后，我们基于不同骨干网络对现有盲运动去模糊方法进行分类和总结，包括卷积神经网络、生成对抗网络、循环神经网络和Transformer网络。在此基础上，不仅详细阐述这些不同类别的基本原理，还对其优缺点进行综合总结与比较。通过在四个广泛使用的数据集上进行的定性和定量实验结果，进一步对比了SOTA方法的性能。最后，分析现有挑战与未来方向。所有收集的模型、基准数据集、源代码链接及评估代码已在https://github.com/VisionVerse/Blind-Motion-Deblurring-Survey 公开提供。