MSSPUNet: phase unwrapping using a multi-scale, multi-stage deep neural network for digital holographic tomography

Digital holographic tomography (DHT) is an advanced phase-imaging-based measurement technique widely used for 3D reconstruction. However, the generated phase images often suffer from significant noise interference and irregular distortions, posing challenges for accurate reconstruction. Phase unwrapping, a critical preprocessing step for 3D reconstruction in holographic tomography, is essential to correct phase discontinuities. Traditional phase unwrapping methods frequently lack the robustness and reliability required for practical applications. To address these limitations, we explore deep learning approaches and identify that existing frameworks predominantly rely on single-stage methods, which suffer from inadequate multi-scale feature fusion and a lack of phase continuity constraints, hindering high-precision cross-scale phase unwrapping. To overcome these challenges, we propose MSSPUNet, a multi-scale, multi-stage transformer network, which leverages latent features across different scales to enhance cross-scale feature fusion. This approach achieves synergistic optimization in noise suppression, phase jump correction, and detail preservation. The network was trained on extensive simulated datasets and benchmarked against several existing phase unwrapping methods. Furthermore, we validated its performance using real DHT images of cells, organoids, phantoms, and conventional 3D-printed structures. Experimental results demonstrate that MSSPUNet offers superior accuracy, enhanced robustness, and stronger generalization capabilities compared to existing methods.