Fringe patterns from different optical measurement systems are widely used in scientific and engineering applications. However, fringe patterns are often corrupted by speckle noise, which is necessary to be removed to accurately recover the information encoded in the phase of the fringe pattern. In this paper we propose a lightweight residual dense neural network based on the U-net neural network model (LRDUNet) for fringe pattern denoising. The encoding and decoding layers of the LRDUNet consist of grouped densely connected convolutional layers for the sake of reusing the feature maps and reducing the number of trainable parameters. Additionally, local residual learning is used to avoid the vanishing gradient problem and speed up the learning process. We compare the proposed method versus state-of-the-art methods and present a study of parameters where we demonstrate that computationally simpler versions of the proposed model are still quite competitive. Experiments on simulated and real fringe patterns show that the proposed method outperforms state-of-the-art methods by restoring the main features of the fringe patterns, achieving an average of 41 dB of PSNR on simulated images.

来自不同光学测量系统的条纹图案广泛用于科学和工程应用。然而，条纹图案经常被散斑噪声破坏，必须去除散斑噪声以准确恢复条纹图案相位中编码的信息。在本文中，我们提出了一种基于 U-net 神经网络模型 (LRDUNet) 的轻量级残差密集神经网络，用于条纹图案去噪。LRDUNet 的编码和解码层由成组的密集连接的卷积层组成，以便重用特征图并减少可训练参数的数量。此外，局部残差学习用于避免梯度消失问题并加快学习过程。我们比较了所提出的方法与最先进的方法，并提出了一项参数研究，我们证明了所提出模型的计算简单版本仍然具有竞争力。对模拟和真实条纹图案的实验表明，所提出的方法通过恢复条纹图案的主要特征，优于最先进的方法，在模拟图像上实现了平均 41 dB 的 PSNR。