  Most existing methods usually formulate the non-blind deconvolution problem
into a maximum-a-posteriori framework and address it by manually designing
kinds of regularization terms and data terms of the latent clear images.
However, explicitly designing these two terms is quite challenging and usually
leads to complex optimization problems which are difficult to solve. In this
paper, we propose an effective non-blind deconvolution approach by learning
discriminative shrinkage functions to implicitly model these terms. In contrast
to most existing methods that use deep convolutional neural networks (CNNs) or
radial basis functions to simply learn the regularization term, we formulate
both the data term and regularization term and split the deconvolution model
into data-related and regularization-related sub-problems according to the
alternating direction method of multipliers. We explore the properties of the
Maxout function and develop a deep CNN model with a Maxout layer to learn
discriminative shrinkage functions to directly approximate the solutions of
these two sub-problems. Moreover, given the fast-Fourier-transform-based image
restoration usually leads to ringing artifacts while conjugate-gradient-based
approach is time-consuming, we develop the Conjugate Gradient Network to
restore the latent clear images effectively and efficiently. Experimental
results show that the proposed method performs favorably against the
state-of-the-art ones in terms of efficiency and accuracy.
