  We present a simple and effective approach for non-blind image deblurring,
combining classical techniques and deep learning. In contrast to existing
methods that deblur the image directly in the standard image space, we propose
to perform an explicit deconvolution process in a feature space by integrating
a classical Wiener deconvolution framework with learned deep features. A
multi-scale feature refinement module then predicts the deblurred image from
the deconvolved deep features, progressively recovering detail and small-scale
structures. The proposed model is trained in an end-to-end manner and evaluated
on scenarios with both simulated and real-world image blur. Our extensive
experimental results show that the proposed deep Wiener deconvolution network
facilitates deblurred results with visibly fewer artifacts. Moreover, our
approach quantitatively outperforms state-of-the-art non-blind image deblurring
methods by a wide margin.
