  Blind image deblurring plays a very important role in many vision and
multimedia applications. Most existing works tend to introduce complex priors
to estimate the sharp image structures for blur kernel estimation. However, it
has been verified that directly optimizing these models is challenging and easy
to fall into degenerate solutions. Although several experience-based heuristic
inference strategies, including trained networks and designed iterations, have
been developed, it is still hard to obtain theoretically guaranteed accurate
solutions. In this work, a collaborative learning framework is established to
address the above issues. Specifically, we first design two modules, named
Generator and Corrector, to extract the intrinsic image structures from the
data-driven and knowledge-based perspectives, respectively. By introducing a
collaborative methodology to cascade these modules, we can strictly prove the
convergence of our image propagations to a deblurring-related optimal solution.
As a nontrivial byproduct, we also apply the proposed method to address other
related tasks, such as image interpolation and edge-preserved smoothing. Plenty
of experiments demonstrate that our method can outperform the state-of-the-art
approaches on both synthetic and real datasets.
