  While neural networks have achieved vastly enhanced performance over
traditional iterative methods in many cases, they are generally empirically
designed and the underlying structures are difficult to interpret. The
algorithm unrolling approach has helped connect iterative algorithms to neural
network architectures. However, such connections have not been made yet for
blind image deblurring. In this paper, we propose a neural network architecture
that advances this idea. We first present an iterative algorithm that may be
considered a generalization of the traditional total-variation regularization
method on the gradient domain, and subsequently unroll the half-quadratic
splitting algorithm to construct a neural network. Our proposed deep network
achieves significant practical performance gains while enjoying
interpretability at the same time. Experimental results show that our approach
outperforms many state-of-the-art methods.
