  Image deblurring, a.k.a. image deconvolution, recovers a clear image from
pixel superposition caused by blur degradation. Few deep convolutional neural
networks (CNN) succeed in addressing this task. In this paper, we first
demonstrate that the minimum-mean-square-error (MMSE) solution to image
deblurring can be interestingly unfolded into a series of residual components.
Based on this analysis, we propose a novel iterative residual deconvolution
(IRD) algorithm. Further, IRD motivates us to take one step forward to design
an explicable and effective CNN architecture for image deconvolution.
Specifically, a sequence of residual CNN units are deployed, whose intermediate
outputs are then concatenated and integrated, resulting in concatenated
residual convolutional network (CRCNet). The experimental results demonstrate
that proposed CRCNet not only achieves better quantitative metrics but also
recovers more visually plausible texture details compared with state-of-the-art
methods.
