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调控经散射介质散射后的光场在生物组织成像、军事反恐和光信息传输等领域具有潜在的应用价值. 然而, 经散射介质反射后的光子传播方向变得无序, 导致携带光信息的波前被扰乱. 将一种新的波前振幅调制方法即自适应遗传算法(self-adaption genetic algorithm, SAGA)引入到背向散射光场调控中. 根据环境变化, 种群自适应的选择基因的突变或交叉, 极大地提高了寻找最优解的收敛速度. 通过实验研究验证了SAGA在背向散射光场调控方面的有效性, 并表明相较于遗传算法(genetic algorithm, GA), SAGA在调控速度和抗噪声方面都存在明显优势. 研究结果表明, SAGA在较少的迭代次数内即可获得高对比度的光聚焦和图像投影, 并收敛于最优解. 相较于GA, 其在进行散射聚焦和图像投影时的调控速度分别快8.3倍和14.38倍. 这种快速的散射光场调控技术为光信息传输领域的研究提供了新思路, 具有广泛的应用潜力.Modulating the light field scattered by scattering media has potential applications in biological tissue imaging, military anti-terrorism, and optical information transmission. However, light reflected by complex scattering media, such as biological tissues, clouds and fog, multi-mode fiber, and white paper, will produce disorderly scattering, and then disturb the wavefront of incident light. It has always been the main obstacle to optical imaging and effective information transmission. Therefore, the control of backscattered light field is also a research field worthy of attention, which is of great significance for the transmission of non-line-of-sight optical information. It is also very important to find a method of efficiently controlling backscattered light field for the breakthrough of related applications. It has been found that iterative wavefront shaping technology is an effective solution, which gradually modulates the amplitude or phase distribution of wavefront according to the feedback of the light intensity distribution in the target area of charge coupled device (CCD). An improved genetic algorithm, self-adaptation genetic algorithm (SAGA), is proposed, which can be used to rapidly modulate the backscattered light field. The amplitude distribution of wavefront is controlled, which makes it form the required pattern at the target position through the interference of light. During the implementation of the algorithm, the SAGA performs gene crossover and mutation separately, and selects gene crossover and mutation operations according to the number of iterations. At the beginning of evolution, the probability of selecting gene mutations is higher because the population needs to adapt to the environment, while at the end of evolution, the probability of selecting gene mutations is lower because it gradually adapts to the environment. In the experimental measurement, the effective modulation area of digital-micromirror device (DMD) is 1024×1024, which is divided into 64×64 modulation segments by pixel merging. Each segment number is assigned a value of 0 or 1. Focusing and image projection performance of scattered light field are evaluated based on peak-to-background ratio (PBR) and Pearson correlation coefficient (Cor), respectively. By comparing the scattered light focusing and image projection of SAGA and genetic algorithm (GA), it is found that SAGA can accurately control the backscattered light field and converge to the optimal value in a few iterations. After 1000 iterations, the GA still has a clear speckle background. With the increase of iteration times, GA will also show bright focus and clear projection image. Compared with GA, SAGA has a modulation speed that is 8.3 times faster in light focusing and 14.38 times faster in image projection, greatly improving the modulation speed of the scattered light field. The fast control technology for scattered light field can lead to numerous new optical communication applications and offer fresh insights into the study of optics and information science.
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Keywords:
- scattering medium /
- wavefront shaping /
- focusing /
- image projection
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图 1 背向散射光场调控原理图 (a)经散射介质反射的光场分布; (b)调制后的反射光场分布; (c) SAGA散射波前整形流程图
Fig. 1. Schematics of modulation of backscattering field: (a) Distribution of light field reflected by scattering medium; (b) distribution of the reflected light field after modulation; (c) flowchart of SAGA.
图 3 SAGA和GA的背向散射聚焦实验结果 (a)迭代过程中的最佳聚焦模式; (b) 1000次迭代SAGA和GA的PBR值随迭代次数的变化; (c) 3000次迭代GA的PBR值随迭代次数的变化
Fig. 3. Experimental results of backscatter focusing: (a) Optimal focusing mode in iterative process; (b) variation curve of PBR of SAGA and GA with 1000 iteration times; (c) variation curve of PBR of GA with 3000 iteration times.
图 4 SAGA和GA的图像投影实验结果 (a)目标图像; (b) SAGA在1000次迭代过程中的最佳投影结果; (c) GA在1000次迭代过程中的最佳投影结果; (d) GA在5000次迭代过程中的最佳投影结果; (e) 1000次迭代SAGA和GA的Cor值随迭代次数的变化; (f) 5000次迭代GA的Cor值随迭代次数的变化
Fig. 4. Experimental results of image projection: (a) Target images; (b) the optimal image projection of SAGA with 1000 iteration times; (c) the optimal image projection of GA with 1000 iteration times; (d) the optimal image projection of GA with 3000 iteration times; (e) variation curve of Cor of SAGA and GA with 1000 iteration times; (f) variation curve of Cor of GA with 5000 iteration times.
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[1] Yaqoob Z, Psaltis D, Feld M S, Yang C 2008 Nat. Photonics 2 110
Google Scholar
[2] Ni F, Liu H, Zheng Y, Chen X 2023 Adv. Photonics 5 046010
[3] Bian Y, Wang F, Wang Y, Fu Z, Liu H, Yuan H, Situ G 2024 Photonics Res. 12 134
Google Scholar
[4] 段美刚, 赵映, 左浩毅 2024 73 124203
Google Scholar
Duan M G, Zhao Y, Zuo H Y 2024 Acta Phys. Sin. 73 124203
Google Scholar
[5] Zhang X, Gao J, Gan Y, Song C, Zhang D, Zhuang S, Han S, Lai P, Liu H 2023 PhotoniX 4 10
Google Scholar
[6] Mclntosh R, Goetschy A, Bender N, Yamilov A, Hsu C, Yılmaz H, Cao H 2024 Nat. Photonics 18 744
Google Scholar
[7] Wu C, Liu J, Huang X, Li Z P, Yu C, Ye J T, Zhang J, Zhang Q, Dou X, Goyal V K, Xu F, Pan J W 2021 Nat. Photonics 118 e2024468118
[8] 孙雪莹, 刘飞, 段景博, 牛耕田, 邵晓鹏 2021 70 224203
Google Scholar
Sun X Y, Liu F, Duan J B, Niu G T, Shao X P 2021 Acta Phys. Sin. 70 224203
Google Scholar
[9] 张熙程, 方龙杰, 庞霖 2018 67 104202
Google Scholar
Zhang X C, Fang L J, Pang L 2018 Acta Phys. Sin. 67 104202
Google Scholar
[10] Ding C, Shao R, Qu Y, He Q, Liu L, Yang J 2023 Laser Photonics Rev. 17 2300104
Google Scholar
[11] 相萌, 何飘, 王天宇, 袁琳, 邓凯, 刘飞, 邵晓鹏 2024 73 124202
Google Scholar
Xiang M, He P, Wang T Y, Yuan L, Deng K, Liu F, Shao X P 2024 Acta Phys. Sin. 73 124202
Google Scholar
[12] Shi A D, Wang Z Y, Duan C X, Wang Z, Zhang W L 2024 Chin. Phys. B 33 104202
Google Scholar
[13] 沈乐成, 罗嘉伟, 张志凌, 张诗按 2024 光学学报 44 1026016
Google Scholar
Shen Y C, Luo J W, Zhang Z L, Zhang S A 2024 Acta Opt. Sin. 44 1026016
Google Scholar
[14] 朱磊, 邵晓鹏 2020 光学学报 40 0111005
Google Scholar
Zhu L, Shao X P 2020 Acta Opt. Sin. 40 0111005
Google Scholar
[15] Cao Z Z, Zhang X B, Osnabrugge G, Li J H, Vellekoop I M, Koonen A M 2019 Light-Sci. Appl. 8 69
Google Scholar
[16] Tzang O, Caravaca-Aguirre A M, Wagner K, Piestun R 2018 Nat. Photonics 12 368
Google Scholar
[17] Teğin U, Rahmani B, Kakkava E, Borhani N, Moser C, Psaltis D 2020 APL Photonics 5 030804
Google Scholar
[18] Qiao Y Q, Peng Y J, Zheng Y L, Ye F, Chen X 2018 Opt. Lett. 43 787
Google Scholar
[19] 倪枫超, 刘海港, 陈险峰 2024 光学学报 44 1026006
Google Scholar
Ni F C, Liu H G, Chen X F 2024 Acta Opt. Sin. 44 1026006
Google Scholar
[20] Vellekoop I M, Mosk A P 2007 Opt. Lett. 32 2309
Google Scholar
[21] Liu J, Feng Y, Li W, Xiang M, Xi T, Liu F, Li G, Shao X 2023 Opt. Lett. 48 4077
Google Scholar
[22] Wan L, Chen Z, Huang H, Pu J 2016 Appl. Phys. B 122 204
[23] Peng T, Li R, An S, Yu X, Zhou M, Bai C, Liang Y, Lei M, Zhang G, Yao B, Zhang P 2019 Opt. Express 27 4858
Google Scholar
[24] Yang J, He Q, Liu L, Qu Y, Shao R, Song B, Zhao Y 2021 Light- Sci. Appl. 10 149
Google Scholar
[25] Wang X, Zhao W, Zhai A, Wang D 2023 Opt. Express 31 32287
Google Scholar
[26] Zhang C, Yao Z, Liu T, Sui X, Chen Q, Xie Z, Liu G 2024 Opt. Laser Technol. 169 110018
Google Scholar
[27] Woo C M, Zhao Q, Zhong T, Li H, Yu Z, Lai P 2022 APL Photonics 7 046109
Google Scholar
[28] Li W, He W, Dai Y, Zuo H, Pang L 2024 Opt. Laser Technol. 175 110740
Google Scholar
[29] Zhao Y, He Q, Li S, Yang J 2021 Opt. Lett. 46 1518
Google Scholar
[30] Li H H, Woo C M, Zhong T T, Yu Z P, Luo Y Q, Zheng Y J, Yang X, Hui H, Lai P X 2021 Photonics Res. 9 202
Google Scholar
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Google Scholar
[32] Deb K, Beyer H G 2001 Evol. Comput. 9 197
Google Scholar
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Google Scholar
[34] Hinterding R, Michalewicz Z, Peachey T C 1996 International Conference on Evolutionary Computation—The 4th International Conference on Parallel Problem Solving from Nature, Berlin Germany, September 22–26, 1996 pp420–429
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