Laser & Optoelectronics Progress, Volume. 60, Issue 16, 1610003(2023)
Phase Recovery of Electronic Speckle Interferometric Fringe Pattern Using Deep Learning
Fig. 1. U-Net network structure
Fig. 2. Schematic of sub-pixel convolution upsampling
Fig. 3. DSSINet network structure
Fig. 4. USS-Net structure
Fig. 5. SSIF dataset examples
Fig. 6. ESIF dataset examples
Fig. 7. Schematic of phase recovery effect of SSIF dataset. (a) Simulated fringe image; (b) network output diagram; (c) true package phase value; (d) unfolding phase of network output diagram; (e) true unwrapping phase value
Fig. 8. Schematic of phase recovery effect of ESIF dataset. (a) Experimental fringe image; (b) network output diagram; (c) wrapped phase value obtained by four-step phase shifting method; (d) unfolding phase of network output diagram; (e) true unwrapping phase value
Fig. 9. Unfolded phase error analysis. (a)-(e) Phase error corresponding to the five experimental fringe patterns in Fig. 8 respectively
|
|
Get Citation
Copy Citation Text
Fang Zhang, Wenheng Li, Wen Wang, Rui Zhao. Phase Recovery of Electronic Speckle Interferometric Fringe Pattern Using Deep Learning[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1610003
Category: Image Processing
Received: Aug. 12, 2022
Accepted: Oct. 9, 2022
Published Online: Aug. 15, 2023
The Author Email: Wang Wen (wangwen@tiangong.edu.cn)