  A motion-blurred image is the temporal average of multiple sharp frames over
the exposure time. Recovering these sharp video frames from a single blurred
image is nontrivial, due to not only its strong ill-posedness, but also various
types of complex motion in reality such as rotation and motion in depth. In
this work, we report a generalized video extraction method using the affine
motion modeling, enabling to tackle multiple types of complex motion and their
mixing. In its workflow, the moving objects are first segemented in the alpha
channel. This allows separate recovery of different objects with different
motion. Then, we reduce the variable space by modeling each video clip as a
series of affine transformations of a reference frame, and introduce the
$l0$-norm total variation regularization to attenuate the ringing artifact. The
differentiable affine operators are employed to realize gradient-descent
optimization of the affine model, which follows a novel coarse-to-fine strategy
to further reduce artifacts. As a result, both the affine parameters and sharp
reference image are retrieved. They are finally input into stepwise affine
transformation to recover the sharp video frames. The stepwise retrieval
maintains the nature to bypass the frame order ambiguity. Experiments on both
public datasets and real captured data validate the state-of-the-art
performance of the reported technique.
