This paper presents a supervised data-driven algorithm for impulse noise removal via iterative scheme-inspired network (IIN). IIN is defined over a data flow graph, which is derived from the iterative procedures in Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing the L1-guided variational model. In the training phase, the L1-minimization is reformulated into an augmented Lagrangian scheme through adding a new auxiliary variable. In the testing phase, it has computational overhead similar to ADMM but uses optimized parameters learned from the training data for restoration task. Experimental results demonstrate that the newly proposed method can obtain very significantly superior performance than current state-of-the-art variational and dictionary learning-based approaches for salt-and-pepper noise removal.

中文翻译：

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启发式迭代方案的网络，用于消除脉冲噪声

本文提出了一种受监督的数据驱动算法，用于通过迭代方案启发式网络（IIN）去除脉冲噪声。IIN是在数据流图上定义的，该数据流图是从乘法器交替方向方法（ADMM）算法的迭代过程中得出的，用于优化L1引导的变分模型。在训练阶段，通过添加新的辅助变量，将L1最小化重新构造为增强的Lagrangian方案。在测试阶段，它具有类似于ADMM的计算开销，但是使用从训练数据中学到的优化参数来执行恢复任务。实验结果表明，新提出的方法可以比当前最先进的基于变分和字典学习的盐和胡椒噪声去除方法获得更好的性能。