Efficient Non-Blind Image Deblurring With Discriminative Shrinkage Deep Networks

Most existing non-blind deblurring methods formulate the problem into a maximum-a-posteriori framework and address it by manually designing a variety of regularization terms and data terms of the latent clear images. However, explicitly designing these two terms is quite challenging, which usually leads to complex optimization problems. In this paper, we propose a Discriminative Shrinkage Deep Network for fast and accurate deblurring. Most existing methods use deep convolutional neural networks (CNNs), or radial basis functions only to learn the regularization term. In contrast, we formulate both the data and regularization terms while splitting the deconvolution model into data-related and regularization-related sub-problems. We explore the properties of the Maxout function and develop a deep CNN model with Maxout layers to learn discriminative shrinkage functions, which directly approximate the solutions of these two sub-problems. Moreover, we develop a U-Net according to Krylov subspace method to restore the latent clear images effectively and efficiently, which plays a role but is better than the conventional fast-Fourier-transform-based or conjugate gradient method. Experimental results show that the proposed method performs favorably against the state-of-the-art methods regarding efficiency and accuracy.