Chinese Journal of Lasers, Volume. 50, Issue 19, 1914001(2023)
Deep Learning for Reconstruction of Continuous Terahertz In‐Line Digital Holography
Fig. 2. Network structure and algorithm block diagram. (a) U-net network structure;(b)H-UnetM block diagram;(c)AS-UnetM block diagram
Fig. 4. Label images, holograms at 19 mm and reconstructed images for character A with 0.4 mm resolution, S with 0.4 mm resolution, A with 0.3 mm resolution, S with 0.3 mm resolution, and G with 0.5 mm resolution from left to right. (a) Label images; (b) holograms;(c)reconstructed images of ASM;(d)reconstructed images of APRA;(e)reconstructed images of H-UnetM;(f)reconstructed images of AS-UnetM
Fig. 5. Holograms at 20 mm and reconstructed images for targets same with Fig. 4. (a) Holograms at 20 mm;(b)reconstructed images of ASM;(c)reconstructed images of APRA;(d)reconstructed images of H-UnetM;(e)reconstructed images of AS-UnetM
Fig. 7. Holograms and reconstructed images by different methods.(a)Recording distance is 19 mm;(b)recording distance is 20 mm
Fig. 8. Holograms with different frames and corresponding reconstructed images.(a)Single frame hologram;(b)average holograms of 4 frame
Fig. 9. Standard image, holograms and reconstructed images by different methods.(a)Standard image;(b)simulation results;(c)experimental results
|
|
|
|
Get Citation
Copy Citation Text
Keyang Cheng, Qi Li. Deep Learning for Reconstruction of Continuous Terahertz In‐Line Digital Holography[J]. Chinese Journal of Lasers, 2023, 50(19): 1914001
Category: terahertz technology
Received: Aug. 24, 2022
Accepted: Sep. 28, 2022
Published Online: Sep. 25, 2023
The Author Email: Cheng Keyang (c1092986874@163.com)