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

Abstract

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 a fiber internal structure in holographic diffraction tomography. By taking the sparse-view sinogram as the input and the cross-section image obtained by the dense-view sinogram as the ground truth, the neural network can reconstruct the cross-section image from the sparse-view sinogram. It performs better than the corresponding filtered back-projection algorithm with a sparse-view sinogram, both in the case of simulated data and real experimental data.

Recent Advances and Current Trends in Transmission Tomographic Diffraction Microscopy

“…This review also identifies future tracks to be explored and hot topics to be addressed. These certainly leave TDM development open to a bright future, including the new capabilities enabled by deep-learning approaches, whether for hologram denoising, phase map computations, sample reconstructions, or specimen analysis [ 188 , 222 , 223 , 224 , 225 , 325 , 326 , 327 , 328 , 329 , 330 , 331 , 332 , 333 , 334 , 335 ].…”

Recognition of Multiscale Dense Gel Filament-Droplet Field in Digital Holography With Mo-U-Net

“…Midtvedt, et al [32] developed a weighted average convolutional network to analyze the hologram of single suspended nanoparticle and quantified the size and refractive index of a single subwavelength particle. But until now, these works mainly focus on relatively large [33,34], spherical object [35,36], sparse small particle field [37][38][39] or other objects (e.g., fiber internal structure [40], cell identification [41]), yet few focused on dense particle field consisted of liquid droplets and filaments with various morphological shapes like gel atomization field. And the combination of digital holography and deep learning methods were also extended to other particle-like objects, Belashov, et al [42] utilized holographic microscopy combined with cell segmentation algorithm using machine learning to characterize the dynamic process of apoptosis and the accuracy achieved 95.5% and Wang, et al [43] segmented some terahertz images of gear wheel and used average structural similarity to get the relatively best results which were proved to be better than some traditional segmentation algorithms in their paper.…”