In many real-world scenarios, recorded videos suffer from accidental focus blur, and while video deblurring methods exist, most specifically target motion blur. This paper introduces a framework optimised for the joint task of focal deblurring (refocusing) and video super-resolution (VSR). The proposed method employs novel map guided transformers, in addition to image propagation, to effectively leverage the continuous spatial variance of focal blur and restore the footage. We also introduce a flow re-focusing module to efficiently align relevant features between the blurry and sharp domains. Additionally, we propose a novel technique for generating synthetic focal blur data, broadening the model's learning capabilities to include a wider array of content. We have made a new benchmark dataset, DAVIS-Blur, available. This dataset, a modified extension of the popular DAVIS video segmentation set, provides realistic out-of-focus blur degradations as well as the corresponding blur maps. Comprehensive experiments on DAVIS-Blur demonstrate the superiority of our approach. We achieve state-of-the-art results with an average PSNR performance over 1.9dB greater than comparable existing video restoration methods. Our source code will be made available at https://github.com/crispianm/DaBiT

翻译：在许多实际场景中，录制的视频常受到意外失焦模糊的影响。尽管现有视频去模糊方法众多，但大多专门针对运动模糊。本文提出了一种针对焦点去模糊（重聚焦）与视频超分辨率联合任务的优化框架。该方法除采用图像传播机制外，还引入了新型的映射引导Transformer，以有效利用焦点模糊的连续空间变化特性并恢复视频内容。我们还提出了一种流重聚焦模块，用于高效对齐模糊域与清晰域间的相关特征。此外，我们开发了一种生成合成焦点模糊数据的新技术，通过涵盖更广泛的内容类型来扩展模型的学习能力。我们公开了新的基准数据集DAVIS-Blur，该数据集基于流行的DAVIS视频分割集进行修改扩展，提供真实的失焦模糊退化数据及对应的模糊映射图。在DAVIS-Blur上的综合实验证明了我们方法的优越性，其平均PSNR性能比现有同类视频恢复方法高出1.9dB以上，达到了当前最优效果。源代码将在https://github.com/crispianm/DaBiT 公开。