  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
