Sparse-view imaging of a fiber internal structure in holographic diffraction tomography via a convolutional neural network

• DOI: 10.1364/ao.404276
• Publication Date: 2020-10-29T20:30:08Z
• Authors: Jianglei Di; Wenxuan Han; Sisi Liu; Kaiqiang Wang; Ju Tang; Jianlin Zhao
• Keywords: Ghost Imaging

Deep learning has recently shown great potential in computational imaging. Here, we propose a deep-learning-based reconstruction method to realize the sparse-view imaging of fiber internal structure holographic diffraction tomography. By taking sinogram as input and cross-section image obtained by dense-view ground truth, neural network can reconstruct from sinogram. It performs better than corresponding filtered back-projection algorithm with sinogram, both case simulated data real experimental data.