  Image deblurring in photon-limited conditions is ubiquitous in a variety of
low-light applications such as photography, microscopy, and astronomy. However,
the presence of photon shot noise due to low illumination and/or short exposure
makes the deblurring task substantially more challenging than the conventional
deblurring problems. In this paper, we present an algorithm unrolling approach
for the photon-limited deblurring problem by unrolling a Plug-and-Play
algorithm for a fixed number of iterations. By introducing a three-operator
splitting formation of the Plug-and-Play framework, we obtain a series of
differentiable steps which allows the fixed iteration unrolled network to be
trained end-to-end. The proposed algorithm demonstrates significantly better
image recovery compared to existing state-of-the-art deblurring approaches. We
also present a new photon-limited deblurring dataset for evaluating the
performance of algorithms.
