Abstract We propose a wrapped phase denoising method based on convolutional neural networks (CNN), which can effectively denoise a noisy wrapped phase. The noisy numerator and denominator of the arctangent function are firstly denoised by CNN, and then the filtered numerator and denominator use the arctangent function to obtain the clean wrapped phase. We experimentally verify the denoising performance using various wrapped phase that contains different noise conditions, where the denoised wrapped phase can achieve a satisfactory unwrapping performance using the existing simple unwrapping method. In addition, the proposed method is further demonstrated through the comparison of the existing methods, and shows an accurate denoising result without adjusting any parameters.

中文翻译：

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使用卷积神经网络的环绕相位去噪

摘要 我们提出了一种基于卷积神经网络（CNN）的包裹相位去噪方法，可以有效地对嘈杂的包裹相位进行去噪。反正切函数的含噪分子和分母首先通过CNN去噪，过滤后的分子和分母使用反正切函数得到干净的包裹相位。我们实验验证了使用包含不同噪声条件的各种包裹相位的去噪性能，其中去噪包裹相位可以使用现有的简单展开方法获得令人满意的展开性能。此外，通过与现有方法的比较进一步证明了所提出的方法，并且在不调整任何参数的情况下显示了准确的去噪结果。