Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …

Direct laser writing methods based on two‐photon polymerization (2PP) are powerful tools
for the on‐demand printing of precise and complex 3D architectures at the micro and …

Convolutional neural networks (CNNs) have gained tremendous success in solving complex
inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct …

Recent advances in quantitative phase imaging (QPI) and artificial intelligence (AI) have
opened up the possibility of an exciting frontier. The fast and label-free nature of QPI …

Imaging through scattering media is one of the hotspots in the optical field, and impressive
results have been demonstrated via deep learning (DL). However, most of the DL …

摘要近年来, 光学成像技术已经由传统的强度, 彩色成像发展进入计算光学成像时代.
计算光学成像基于几何光学, 波动光学等理论对场景目标经光学系统成像再到探测器采样这一 …

We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography
(DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high …

Traditional digital holographic imaging algorithms need multiple iterations to obtain focused
reconstructed image, which is time-consuming. In terms of phase retrieval, there is also the …

A Transformer-based deep direct sampling method is proposed for electrical impedance
tomography, a well-known severely ill-posed nonlinear boundary value inverse problem. A …

We present a machine learning-based method for tomographic reconstruction of dense
layered objects, with range of projection angles limited to±10○. Whereas previous …