Blur artifacts can seriously degrade the visual quality of images, and numerous deblurring methods have been proposed for specific scenarios. However, in most real-world images, blur is caused by different factors, e.g., motion and defocus. In this paper, we address how different deblurring methods perform in the case of multiple types of blur. For in-depth performance evaluation, we construct a new large-scale multi-cause image deblurring dataset (called MC-Blur), including real-world and synthesized blurry images with mixed factors of blurs. The images in the proposed MC-Blur dataset are collected using different techniques: averaging sharp images captured by a 1000-fps high-speed camera, convolving Ultra-High-Definition (UHD) sharp images with large-size kernels, adding defocus to images, and real-world blurry images captured by various camera models. Based on the MC-Blur dataset, we conduct extensive benchmarking studies to compare SOTA methods in different scenarios, analyze their efficiency, and investigate the built dataset's capacity. These benchmarking results provide a comprehensive overview of the advantages and limitations of current deblurring methods, and reveal the advances of our dataset.

翻译：模糊伪影会严重降低图像的视觉质量，目前已针对特定场景提出了多种去模糊方法。然而，在大多数真实世界图像中，模糊由不同因素（如运动模糊和散焦模糊）共同导致。本文探讨了不同去模糊方法在面对多种类型模糊时的表现。为了进行深入性能评估，我们构建了一个新型大规模多因素图像去模糊数据集（称为MC-Blur），包含混合模糊因素的现实和合成模糊图像。该数据集通过多种技术采集：对1000帧/秒高速相机拍摄的清晰图像进行平均处理、将超高清晰度清晰图像与大型卷积核进行卷积、为图像添加散焦效果，以及通过不同相机型号采集真实模糊图像。基于MC-Blur数据集，我们开展了广泛的基准测试研究：比较不同场景下最先进方法的性能，分析其效率，并评估构建数据集的泛化能力。这些基准测试结果全面揭示了当前去模糊方法的优势与局限，同时展示了我们数据集的先进性。