A Novel Learnable Gradient Descent Type Algorithm for Non-convex Non-smooth Inverse Problems

• URL: http://arxiv.org/abs/2003.06748v2
• Date: Tue, 24 Mar 2020 23:39:46 GMT
• Title: A Novel Learnable Gradient Descent Type Algorithm for Non-convex Non-smooth Inverse Problems
• Authors: Qingchao Zhang, Xiaojing Ye, Hongcheng Liu, and Yunmei Chen
• Abstract summary: We propose a novel type to solve inverse problems consisting general architecture and neural intimating. Results that the proposed network outperforms the state reconstruction methods on different image problems in terms of efficiency and results.
• Score: 3.888272676868008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Optimization algorithms for solving nonconvex inverse problem have attracted significant interests recently. However, existing methods require the nonconvex regularization to be smooth or simple to ensure convergence. In this paper, we propose a novel gradient descent type algorithm, by leveraging the idea of residual learning and Nesterov's smoothing technique, to solve inverse problems consisting of general nonconvex and nonsmooth regularization with provable convergence. Moreover, we develop a neural network architecture intimating this algorithm to learn the nonlinear sparsity transformation adaptively from training data, which also inherits the convergence to accommodate the general nonconvex structure of this learned transformation. Numerical results demonstrate that the proposed network outperforms the state-of-the-art methods on a variety of different image reconstruction problems in terms of efficiency and accuracy.

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