论文标题
在图像恢复的背景下，Deeppdnet的替代设计
Alternative design of DeepPDNet in the context of image restoration
论文作者
论文摘要
这项工作设计了一个图像恢复深网，依靠展开的Chambolle-Pock原始二次迭代。当指定$ \ ell_2 $ -norm数据期和分析稀疏先验时，我们网络的每一层都是由Chambolle-Pock迭代构建的。我们网络的参数是chambolle-pock方案的阶跃尺寸和涉及基于稀疏性的惩罚的线性操作员，包括隐式正规化参数。充分描述了反向传播过程。初步实验说明了在BSD68数据库上图像恢复的上下文中，这种深度原始偶偶联网络的良好行为。
This work designs an image restoration deep network relying on unfolded Chambolle-Pock primal-dual iterations. Each layer of our network is built from Chambolle-Pock iterations when specified for minimizing a sum of a $\ell_2$-norm data-term and an analysis sparse prior. The parameters of our network are the step-sizes of the Chambolle-Pock scheme and the linear operator involved in sparsity-based penalization, including implicitly the regularization parameter. A backpropagation procedure is fully described. Preliminary experiments illustrate the good behavior of such a deep primal-dual network in the context of image restoration on BSD68 database.
