  Blind deconvolution is a challenging problem, but in low-light it is even
more difficult. Existing algorithms, both classical and deep-learning based,
are not designed for this condition. When the photon shot noise is strong,
conventional deconvolution methods fail because (1) the image does not have
enough signal-to-noise ratio to perform the blur estimation; (2) While deep
neural networks are powerful, many of them do not consider the forward process.
When the noise is strong, these networks fail to simultaneously deblur and
denoise; (3) While iterative schemes are known to be robust in the classical
frameworks, they are seldom considered in deep neural networks because it
requires a differentiable non-blind solver.
  This paper addresses the above challenges by presenting an
\emph{unsupervised} blind deconvolution method. At the core of this method is a
reformulation of the general blind deconvolution framework from the
conventional image-kernel alternating minimization to a purely kernel-based
minimization. This kernel-based minimization leads to a new iterative scheme
that backpropagates an unsupervised loss through a pre-trained non-blind solver
to update the blur kernel. Experimental results show that the proposed
framework achieves superior results than state-of-the-art blind deconvolution
algorithms in low-light conditions.
