Mr. Longjie Fang Profile

Longjie Fang

at Sichuan Univ

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Proceedings Article | 13 February 2018 Paper

Focusing light through random photonic layers by four-element division algorithm

Longjie Fang, Xicheng Zhang, Haoyi Zuo, Lin Pang

Proceedings Volume 10492, 1049216 (2018) https://doi.org/10.1117/12.2288207

KEYWORDS: Light scattering, Spatial light modulators, Scattering, Modulation, Geometrical optics, Optimization (mathematics), Phase shift keying, Signal to noise ratio, Detection and tracking algorithms, Charge-coupled devices

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The propagation of waves in turbid media is a fundamental problem of optics with vast applications. Optical phase optimization approaches for focusing light through turbid media using phase control algorithm have been widely studied in recent years due to the rapid development of spatial light modulator. The existing approaches include element-based algorithms - stepwise sequential algorithm, continuous sequential algorithm and whole element optimization approaches - partitioning algorithm, transmission matrix approach and genetic algorithm. The advantage of element-based approaches is that the phase contribution of each element is very clear; however, because the intensity contribution of each element to the focal point is small especially for the case of large number of elements, the determination of the optimal phase for a single element would be difficult. In other words, the signal to noise ratio of the measurement is weak, leading to possibly local maximal during the optimization. As for whole element optimization approaches, all elements are employed for the optimization. Of course, signal to noise ratio during the optimization is improved. However, because more random processings are introduced into the processing, optimizations take more time to converge than the single element based approaches. Based on the advantages of both single element based approaches and whole element optimization approaches, we propose FEDA approach. Comparisons with the existing approaches show that FEDA only takes one third of measurement time to reach the optimization, which means that FEDA is promising in practical application such as for deep tissue imaging.

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