Photon-Limited Deblurring Using Algorithm Unrolling

Image deblurring in a photon-limited condition is ubiquitous in a variety of low-light applications such as photography, microscopy and astronomy. However, the presence of photon shot noise due to a low-illumination and/or short exposure makes the deblurring task substantially more challenging than conventional deblurring. In this paper we present an algorithm unrolling approach that unrolls a Plug-and-Play algorithm using a fixed-iteration network. By changing the conventional two-variable splitting formulation of Plug-and-Play to an alternate three-variable splitting, we obtain a differentiable and end-to-end trainable network. Our algorithm outperforms existing methods for 1dB across different illuminations. We also overcome the difficulty of acquiring real motion blur kernels at low-light by presenting a photon-limited motion deblurring image dataset.