  The lack of interpretability in current deep learning models causes serious
concerns as they are extensively used for various life-critical applications.
Hence, it is of paramount importance to develop interpretable deep learning
models. In this paper, we consider the problem of blind deconvolution and
propose a novel model-aware deep architecture that allows for the recovery of
both the blur kernel and the sharp image from the blurred image. In particular,
we propose the Deep Unfolded Richardson-Lucy (Deep-URL) framework -- an
interpretable deep-learning architecture that can be seen as an amalgamation of
classical estimation technique and deep neural network, and consequently leads
to improved performance. Our numerical investigations demonstrate significant
improvement compared to state-of-the-art algorithms.
