Acta Optica Sinica, Volume. 42, Issue 6, 0626001(2022)
Feedback Wavefront Manipulation Method Based on Genetic Gradient Particle Swarm Optimization Algorithm Under Strong Noise
Fig. 4. Influence of different parameters on GGPSO algorithm. (a) β; (b) MR; (c) CR
Fig. 5. Light spot signals under numerical simulation and experimental conditions. (a) Experiment without background noise; (b) experiment with background noise; (c) numerical simulation without noise; (d) numerical simulation with noise
Fig. 6. Simulation curves of different algorithms at different noise levels. (a) Without noise; (b) 10 dB; (c) 4 dB; (d) 1 dB
Fig. 7. Optimized phase distribution and focusing effect after scattering sample. (a) Phase distribution; (b) focusing effect
Fig. 8. Experimental device and its plane diagram. (a) Plane diagram; (b) experimental device
Fig. 9. Iterative optimization results of different algorithms. (a) Measurement results; (b) simulation results
Fig. 10. Optimized phase distribution and focusing effect after scattering sample under different algorithms. (a) PSO optimization algorithm; (b) BBPSO optimization algorithm; (c) GGPSO optimization algorithm
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Pingjiang Ling, Yanrui Zhang, Boyu Tian, Keyu Li, Nianchun Sun, Bin Zhang. Feedback Wavefront Manipulation Method Based on Genetic Gradient Particle Swarm Optimization Algorithm Under Strong Noise[J]. Acta Optica Sinica, 2022, 42(6): 0626001
Category: Physical Optics
Received: Jul. 20, 2021
Accepted: Sep. 28, 2021
Published Online: Mar. 8, 2022
The Author Email: Zhang Bin (zhangbinff@sohu.com)