Phase unwrapping in digital holography based on SRDU-net

In digital holographic measurement, the hologram phase is extracted using an inverse tangent function, resulting in a wrapped phase that is constrained to be (-π,π]. However, the phase contains the height information of the object, which is crucial for accurate measurement of the object contour. Therefore, a digital holographic phase unwrapping method based on SRDU-Net (Separable-Residual-Dense-Inverted U-Net) is proposed in this paper. The SRDU-Net network is constructed by introducing depth-separable convolution, inverted residuals and dense blocks with U-Net as the network framework to achieve high-precision hologram phase recovery. This network adopts depth-separable convolution instead of traditional convolution, combines the inverse residual connection to construct a lightweight convolution structure, and defines dense blocks with this structure to form a lightweight grouped deep convolution network. Meanwhile, the Leaky ReLU activation function is used to introduce the learning rate mechanism to optimize the network parameters with Huber and MSE (mean square error) as the combined loss function. The simulated phase dataset is used to train the network, the trained network model is tested for speckle noise immunity, and phase unwrapping experiments are performed on the collected holograms of the test samples. The results show that SRDU-Net improves the SSIM by 0.1% and reduces the RMSE by 86% over Res-UNet (Residual-UNet). Therefore, the proposed method can realize high-precision recovery of digital holographic wrapped phases, and has a good robustness to phase unwrapping of holograms containing a high degree of speckle noise. •Propose a phase unwrapping network for digital holography.•Using U-Net to introduce depth-separable convolution, inverted residuals and dense block to construct the network.•Huber robust loss and mean square error are combined loss functions by weight ratio.•Random matrix enlargement and Gaussian functions superposition generate training data in random proportions.•The network can recover the wrapped phases with better accuracy and continuity.