@techreport{oai:ipsj.ixsq.nii.ac.jp:00203414,
 author = {Jianfeng, Xu and Kazuyuki, Tasaka and Jianfeng, Xu and Kazuyuki, Tasaka},
 issue = {12},
 month = {Feb},
 note = {Most existing video deblurring works focus on the use of temporal redundancy and lack utilization of the prior information about data itself, resulting in strong dataset dependency and limited generalization ability in handling challenging scenarios, like blur in low contrast or severe motion areas, and non-uniform blur. Therefore, we propose a PRiOr-enlightened MOTION-robust video deblurring model (PROMOTION) suitable for both global and local blurry scenarios. On the one hand, we use 3D group convolution to efficiently encode heterogeneous prior information (including illumination, structure, and motion priors), which enhances the model's blur perception while mitigating the output's artifacts. On the other hand, we design the priors representing blur distribution, which enables our model to better handle non-uniform blur in spatio-temporal domain. In addition to the classical camera shake caused blurry scenes, we also prove the generalization of the model for local blur in real scenario, resulting in better accuracy of hand pose estimation., Most existing video deblurring works focus on the use of temporal redundancy and lack utilization of the prior information about data itself, resulting in strong dataset dependency and limited generalization ability in handling challenging scenarios, like blur in low contrast or severe motion areas, and non-uniform blur. Therefore, we propose a PRiOr-enlightened MOTION-robust video deblurring model (PROMOTION) suitable for both global and local blurry scenarios. On the one hand, we use 3D group convolution to efficiently encode heterogeneous prior information (including illumination, structure, and motion priors), which enhances the model's blur perception while mitigating the output's artifacts. On the other hand, we design the priors representing blur distribution, which enables our model to better handle non-uniform blur in spatio-temporal domain. In addition to the classical camera shake caused blurry scenes, we also prove the generalization of the model for local blur in real scenario, resulting in better accuracy of hand pose estimation.},
 title = {A Study on Motion-robust Video Deblurring},
 year = {2020}
}