  This paper tackles the challenging problem of video deblurring. Most of the
existing works depend on implicit or explicit alignment for temporal
information fusion which either increase the computational cost or result in
suboptimal performance due to wrong alignment. In this study, we propose a
factorized spatio-temporal attention to perform non-local operations across
space and time to fully utilize the available information without depending on
alignment. It shows superior performance compared to existing fusion techniques
while being much efficient. Extensive experiments on multiple datasets
demonstrate the superiority of our method.
