  Diffusion model-based inverse problem solvers have demonstrated
state-of-the-art performance in cases where the forward operator is known (i.e.
non-blind). However, the applicability of the method to blind inverse problems
has yet to be explored. In this work, we show that we can indeed solve a family
of blind inverse problems by constructing another diffusion prior for the
forward operator. Specifically, parallel reverse diffusion guided by gradients
from the intermediate stages enables joint optimization of both the forward
operator parameters as well as the image, such that both are jointly estimated
at the end of the parallel reverse diffusion procedure. We show the efficacy of
our method on two representative tasks -- blind deblurring, and imaging through
turbulence -- and show that our method yields state-of-the-art performance,
while also being flexible to be applicable to general blind inverse problems
when we know the functional forms.
