Author Affiliations
1Jiangsu Key Laboratory for Opto-Electronic Technology, School of Physics and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China2School of Electronic Engineering and Optoelectronic Techniques, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, Chinashow less
Fig. 1. Schematic diagram of digital holographic recording
Fig. 2. Schematic of deep neural network
Fig. 3. Network architecture. (a) Convolutional neural network; (b) U-net
[38] Fig. 4. Relationship between error between output and label in training set, test set, and validation set and number of iterations. (a) Training set and test set; (b) validation set
Fig. 5. Deep-learning-based hologram reconstruction. (a) Phase reconstruction of diffraction intensity map based on end-to-end deep learning
[53]; (b) removal of twin images in in-line holographic reconstruction based on deep learning
[28]; (c) deep-learning-based autofocusing and removal of twin-images with extended depth-of-field
[54]; (d) autofocus and extended depth of field in in-line holographic reconstruction based on deep learning
[31]; (e) amplitude and phase reconstruction of off-axis hologram based on deep learning
[55] Fig. 6. Deep-learning-based hologram reconstruction with unpaired data
[57]. (a) Process of network training (
Xi∈
X: hologram;
Yi∈
Y: phase image;
G & F: generator;
DY: determine the authenticity of the images generated by
G;
DX: determine the authenticity of the images generated by
F;
Lcyc(
D,
F): loss of cyclic consistency;
LY(
G,
DY,
X,
Y): antagonistic loss between
DY and
G;
LX(
F,
DX,
Y,
X): antagonistic loss between
DX and
F); (b) reconstruction results (I: untrained hologram of input network for testing; II, III: reconstruction based on traditional methods and deep learning; IV: real phase distribution)
Fig. 7. Deep-learning-based autofocusing and phase reconstruction. (a) Phase recovery of defocus hologram based on deep learning
[26]; (b) holographic self-focusing reconstruction based on end-to-end deep learning
[66] Fig. 8. Deep-learning-based holographic reconstruction noise suppression. (a) Coherent noise suppression without clean data based on deep learning
[77]; (b) speckle noise suppression of in-line holographic reconstructed image based on deep learning
[25]; (c) off-axis hologram speckle noise suppression based on deep learning
[81] Fig. 9. Resolution enhancement of hologram reconstruction based on deep learning
[82] Fig. 10. Deep-learning-based noise-free quantitative phase imaging of in-line holography
[26]. (a) Method to achieve the noise-free quantitative phase image; (b) training dataset generation and network training process
Fig. 11. Deep-learning-based hologram generation
[89] Fig. 12. Deep-learning-based cross-modality image transformations
[91] Fig. 13. High-resolution numerical dark-field microscopy imaging based on deep learning
[98]