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.

通过将 Wiener 去卷积算法与深度学习结合，提出了面向非盲目图像去模糊的一种新方法，通过特征空间中的显式去卷积过程并利用多尺度特征细化模块逐步恢复细节和小尺度结构，训练模型的结果表明，所提出的深度 Wiener 去卷积网络能够缩小众多噪点，并在真实世界图像模糊的情况下优化表现，有着显著的效果。