The denoising of interferometric phase images attracts many researchers. Natural image denoising algorithms based on neural networks are often proposed in the development of deep learning methods. A neural network, In-CNN, is derived from an advanced natural image denoising network and proposed for interferometric phase image denoising. Preactivation and residual learning methods are combined and applied to the function of the neural network nodes. Considering the particularity of the interferometric phase image, we propose a neural network based on the rational application of the preactivation mode and feedforward mapping, which is different from previous natural image denoising networks. We also construct a training set for an interferometric phase image denoising neural network. We experimentally verify that our model performs better than state-of-the-art interferometric phase image denoising methods based on sparse representation and advanced natural image denoising networks. We discuss the complexity of traditional interferometric phase image denoising algorithms to demonstrate the efficiency of the proposed method.

中文翻译：

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基于残差学习的干涉相图像去噪方法

干涉相图像的去噪吸引了许多研究人员。在深度学习方法的开发中经常提出基于神经网络的自然图像去噪算法。神经网络In-CNN源自高级自然图像去噪网络，并提出用于干涉式相位图像去噪。结合了预激活和残差学习方法，并将其应用于神经网络节点的功能。考虑到干涉相位图像的特殊性，我们提出了一种基于神经网络的神经网络，该神经网络是基于预激活模式和前馈映射的合理应用而设计的，与以前的自然图像去噪网络不同。我们还为干涉相位图像降噪神经网络构造了一个训练集。我们通过实验验证了我们的模型比基于稀疏表示和高级自然图像去噪网络的最新干涉相图像去噪方法性能更好。我们讨论了传统干涉式相位图像去噪算法的复杂性，以证明该方法的有效性。