论文标题
稀疏的Anett用于解决深度学习的反问题
Sparse aNETT for Solving Inverse Problems with Deep Learning
论文作者
论文摘要
我们提出了一个稀疏的重建框架（ANETT），以解决反问题。 Opposed to existing sparse reconstruction techniques that are based on linear sparsifying transforms, we train an autoencoder network $D \circ E$ with $E$ acting as a nonlinear sparsifying transform and minimize a Tikhonov functional with learned regularizer formed by the $\ell^q$-norm of the encoder coefficients and a penalty for the distance to the data manifold.我们提出了一种基于基础图像类的样本集训练自动编码器的策略，以便自动编码器独立于前向操作员，并随后适用于特定的正向模型。为稀疏视图CT提供了数值结果，这清楚地证明了Anett对后处理网络的可行性，鲁棒性以及提高的概括能力和稳定性。
We propose a sparse reconstruction framework (aNETT) for solving inverse problems. Opposed to existing sparse reconstruction techniques that are based on linear sparsifying transforms, we train an autoencoder network $D \circ E$ with $E$ acting as a nonlinear sparsifying transform and minimize a Tikhonov functional with learned regularizer formed by the $\ell^q$-norm of the encoder coefficients and a penalty for the distance to the data manifold. We propose a strategy for training an autoencoder based on a sample set of the underlying image class such that the autoencoder is independent of the forward operator and is subsequently adapted to the specific forward model. Numerical results are presented for sparse view CT, which clearly demonstrate the feasibility, robustness and the improved generalization capability and stability of aNETT over post-processing networks.
