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We present a simple and effective approach for non-blind image deblurring,combining classical techniques and deep learning. In contrast to existingmethods that deblur the image directly in the standard image space, we proposeto perform an explicit deconvolution process in a feature space by integratinga classical Wiener deconvolution framework with learned deep features. Amulti-scale cascaded feature refinement module then predicts the deblurredimage from the deconvolved deep features, progressively recovering detail andsmall-scale structures. The proposed model is trained in an end-to-end mannerand evaluated on scenarios with simulated Gaussian noise, saturated pixels, orJPEG compression artifacts as well as real-world images. Moreover, we presentdetailed analyses of the benefit of the feature-based Wiener deconvolution andof the multi-scale cascaded feature refinement as well as the robustness of theproposed approach. Our extensive experimental results show that the proposeddeep Wiener deconvolution network facilitates deblurred results with visiblyfewer artifacts and quantitatively outperforms state-of-the-art non-blind imagedeblurring methods by a wide margin.

DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring