Neural-network-based reconstruction of digital holograms can improve the speed and the
quality of micro-and macro-object images, as well as reduce the noise and suppress the twin …

Reconstruction of 3D scenes from digital holograms is an important task in different areas of
science, such as biology, medicine, ecology, etc. A lot of parameters, such as the object's …

Subject of study. Image reconstruction from digital holograms using neural networks and
quality enhancement of the obtained reconstructed images were considered. Aim of study …

In-line hologram reconstruction with deep learning Page 1 DW2F.2.pdf Imaging and Applied
Optics 2018 (3D, AIO, AO, COSI, DH, IS, LACSEA, LSC, MATH, pcAOP) © OSA 2018 In-line …

We propose a new learning and inferring model that generates digital holograms using
deep neural networks (DNNs). This DNN uses a generative adversarial network, trained to …

We propose and demonstrate a holographic imaging scheme exploiting random
illuminations for recording hologram and then applying numerical reconstruction and twin …

We present a self-supervised hologram reconstruction neural network trained using a
physics-consistency loss, which achieves superior generalization to reconstruct holograms …

We propose a method to suppress the speckle noise and blur effects of the light field
extracted from a hologram using a deep‐learning technique. The light field can be extracted …

We present a non-iterated generative method for computer-generated holography based on
machine learning. A hologram is calculated with a convolutional deep neural network for …

Herein, we proposed a method to recode double-phase holograms (DPHs) with a full
convolutional neural network (FCN) for alleviating the fringes and spatial shifting noises in …