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Active wavefront shaping for controlling and improving multimode fi ber sensor

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Journal of Innovative Optical Health Sciences 2019年第4期12卷 127-135页

作者： Tianting Zhong Zhipeng Yu huanhao li Zihao li Haohong li Puxiang Lai Department of Biomedical Engineering Hong Kong Polytechnic University Hong Kong P.R.China Shenzhen Research Institute Hong Kong Polytechnic University Shenzhen 518057 P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan Hubei 430074P.R.China

Wavefront shaping(WFS)techniques have been used as a powerful tool to control light propagation in complex media,including multimode *** this paper,we propose a new application of WFS for multimode fber-based *** use of a single multimode fiber alone,without any special fabrication,as a sensor based on the light intensity variations is not an easy *** twist effect on multimode fiber is used as an example *** results show that light intensity through the multimode fiber shows no direct relationship with the twist angle,but the correlation coefficient(CC)of speckle patterns ***,if WFS is applied to transform the spatially seemingly random light pattern at the exit of the multimode fiber into an optical *** focal pattern correlation and intensity both can serve to gauge the twist angle,with doubled measurement range and allowance of using a fast point detector to provide the *** further development,WFS may find potentials to facilitate the development of multimode fber-based sensors in a variety of scenarios.

关键词： Multimode fiber wavefront shaping scattering media fiber sensor optical focusing

Artificial intelligence assisted light control and computational imaging through scattering media

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Journal of Innovative Optical Health Sciences 2019年第4期12卷 32-45页

作者： Shengfu Cheng huanhao li Yunqi Luo Yuanjin Zheng Puxiang Lai Department of Biomedical Engineering Hong Kong Polytechnic University Hong Kong SAR College of Material Science and Engineering Sichuan University SichuanP.R.China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

Coherent optical control within or through scattering media via wavefront shaping has seen broad applications since its invention around *** shaping is aimed at overcoming the strong scattering,featured by random interference,namely speckle *** randomness occurs due to the refractive index inhomogeneity in complex media like biological tissue or the modal dispersion in multimode fiber,yet this randomness is actually deterministic and potentially can be time reversal or *** wavefront shaping approaches,such as optical phase conjugation,iterative optimization,and transmission matrix measurement,have been developed to generate tight and intense optical delivery or high-resolution image of an optical object behind or within a scattering *** performance of these modula-tions,however,is far from *** recently,artifcial intelligence has brought new inspirations to this field,providing exciting hopes to tackle the challenges by mapping the input and output optical patterns and building a neuron network that inherently links *** this paper,we survey the developments to date on this topic and briefly discuss our views on how to harness machine learning(deep learning in particular)for further advancements in the field.

关键词： Optical scattering deep learning wavefront shaping adaptive optics computational imaging

Accelerating deep learning with high energy efficiency:From microchip to physical systems

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The Innovation 2022年第4期3卷 9-10页

作者： huanhao li Zhipeng Yu Qi Zhao Tianting Zhong Puxiang Lai Department of Biomedical Engineering Hong Kong Polytechnic UniversityHong KongChina Shenzhen Research Institute Hong Kong Polytechnic UniversityShenzhen 518057China Photonics Research Institute Hong Kong Polytechnic UniversityHong KongChina

In the era of digits and intemet,massive data have been continuously gener-ated from a variety of sources,including video,photo,audio,text,intemet of things,*** is intuitive that more accurate pattems can be obtained by feeding more data for effective analysis;despite the data redundancy,a clearer picture can be delineated for better ***,traditional methods,even in machine learning,do not benefit from the expanding amount of data,whose perfomance nearty saturates when the data collection is large enough(Figure 1A).Such a dilemma emerges due to their limited capability and insuff cient supply of computation power in the past.

关键词： Figure intuitive enough

Towards smart optical focusing:deep learningempowered dynamic wavefront shaping through nonstationary scattering media

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Photonics Research 2021年第8期9卷 I0004-I0020页

作者： YUNQI Luo SUxIA YAN huanhao li PUXIANG LAI YUANJIN ZHENG School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Biomedical Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen 518034China

Optical focusing through scattering media is of great significance yet challenging in lots of scenarios,including biomedical imaging,optical communication,cybersecurity,three-dimensional displays,*** shaping is a promising approach to solve this problem,but most implementations thus far have only dealt with static media,which,however,deviates from realistic ***,we put forward a deep learning-empowered adaptive framework,which is specifically implemented by a proposed Timely-Focusing-Optical-Transformation-Net(TFOTNet),and it effectively tackles the grand challenge of real-time light focusing and refocusing through time-variant media without complicated *** introduction of recursive fine-tuning allows timely focusing recovery,and the adaptive adjustment of hyperparameters of TFOTNet on the basis of medium changing speed efficiently handles the spatiotemporal non-stationarity of the *** and experimental results demonstrate that the adaptive recursive algorithm with the proposed network significantly improves light focusing and tracking performance over traditional methods,permitting rapid recovery of an optical focus from *** is believed that the proposed deep learning-empowered framework delivers a promising platform towards smart optical focusing implementations requiring dynamic wavefront control.

关键词： scattering tuning media

Towards ideal focusing of diffused light via optical wavefront shaping

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Advanced Photonics 2023年第2期5卷 3-5页

作者： huanhao li Zhipeng Yu Tianting Zhong Shengfu Cheng Puxiang Lai Hong Kong Polytechnic University Department of Biomedical EngineeringHong KongChina Hong Kong Polytechnic University Shenzhen Research InstituteShenzhenChina Hong Kong Polytechnic University Photonics Research InstituteHong KongChina

Multiple scattering can significantly scramble the amplitude and phase profile of an optical *** obscures subtle observations but only speckle patterns can be seen,unlike the ballistic regime where the information or the optical field can be identified with limited *** optical manipulation including information transmission and precise focusing is therefore obstructed as light travels deep into turbidmedia such as fog,turbid fluids,and biological tissues.

关键词： field precise speckle

Edge enhancement through scattering media enabled by optical wavefront shaping

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Photonics Research 2020年第6期8卷 954-962页

作者： ZIHAO li ZHIPENG Yu HUI HUI huanhao li TIANTING ZHONG HONGliN LU PUXIANG LA Deparment of Biomedical Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen 518000China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Key Laboratory for Quantum Optics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Currently at:Department of Bioengineeing University of PennsyIvaniaPhiladelphiaPennsylvania 19104USA

Edge enhancement is a fundamental and important topic in imaging and image processing,as perception of edge is one of the keys to identify and comprehend the contents of an *** enhancement can be performed in many ways,through hardware or *** methods,however,have been limited in free space or clear media for optical applications;in scattering media such as biological tissue,light is multiple scattered,and information is scrambled to a form of seemingly random *** desired,it is challenging to accom-plish edge enhancement in the presence of multiple *** this work,we introduce an implementation of optical wavefront shaping to achieve efficient edge enhancement through scattering media by a two-step *** first step is to acquire a hologram after the scattering medium,where information of the edge region is accurately encoded,while that of the nonedge region is intentionally encoded with inadequate *** second step is to decode the edge information by time reversing the scattered *** capability is demonstrated experimentally,and,further,the performance,as measured by the edge enhancement index(EI)and enhancement-to-noise ratio(ENR),can be controlled easily through tuning the beam *** and ENR can be reinforced by^8.5 and^263 folds,*** the best of our knowledge,this is the first demonstration that edge information of a spatial pattern can be extracted through strong turbidity,which can potentially enrich the comprehension of actual images obtained from a complex environment.

关键词： scattering tuning media

Long distance all-optical logic operations through a single multimode fiber empowered by wavefront shaping

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Photonics Research 2024年第3期12卷 587-597页

作者： ZHIPENG YU TIANTING ZHONG huanhao li HAORAN li CHI MAN WOO SHENGFU CHENG SHUMING JIAO HONGliN liU CHAO LU PUXIANG LAI Department of Biomedical Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen 518000China Peng Cheng Laboratory Shenzhen 518055China Key Laboratory for Quantum Optics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Department of Electronic and Information Engineering The Hong Kong Polytechnic UniversityHong Kong SARChina Photonics Research Institute The Hong Kong Polytechnic UniversityHong Kong SARChina

Multimode fibers(MMFs)are a promising solution for high-throughput signal transmission in the time ***,crosstalk among different optical modes within the MMF scrambles input information and creates seemingly random speckle patterns at the *** characterize this process,a transmission matrix(TM)can be used to relate input and output *** innovations use TMs to manipulate the output field by shaping the input wavefront for exciting advances in deep-brain imaging,neuron stimulation,quantum networks,and analog ***,these approaches consider input/output segments as independent,limiting their use for separate signal processing,such as logic *** proposed method,which makes input/output segments as interdependent,adjusts the phase of corresponding output fields using phase bias maps superimposed on input *** superposition enables signal logic operations through a 15-m-long *** experiments,a single optical logic gate containing three basic logic functions and cascading multiple logic gates to handle binary operands is *** operations are performed for multi-bit logic operations,and multiple optical logic gates are reconstructed simultaneously in a single logic gate with polarization *** proposed method may open new avenues for long-range logic signal processing and transmission via MMFs.

关键词： optical polarization fiber

Adaptive optical focusing through perturbed scattering media with a dynamic mutation algorithm

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Photonics Research 2021年第2期9卷 202-212页

作者： huanhao li CHI MAN WOO TIANTING ZHONG ZHIPENG YU YUNQI LUO YUANJIN ZHENG XIN YANG HUI HUI PUXIANG LAI Department of Biomedical Engineering The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute School of Electrical and Electronics Engineering Nanyang Technological University CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of Sciences

Optical imaging through or inside scattering media, such as multimode fiber and biological tissues, has a significant impact in biomedicine yet is considered challenging due to the strong scattering nature of light. In the past decade, promising progress has been made in the field, largely benefiting from the invention of iterative optical wavefront shaping, with which deep-tissue high-resolution optical focusing and hence imaging becomes possible. Most of the reported iterative algorithms can overcome small perturbations on the noise level but fail to effectively adapt beyond the noise level, e.g., sudden strong perturbations. Reoptimizations are usually needed for significant decorrelation to the medium since these algorithms heavily rely on the optimization performance in the previous iterations. Such ineffectiveness is probably due to the absence of a metric that can gauge the deviation of the instant wavefront from the optimum compensation based on the concurrently measured optical *** this study, a square rule of binary-amplitude modulation, directly relating the measured focusing performance with the error in the optimized wavefront, is theoretically proved and experimentally validated. With this simple rule, it is feasible to quantify how many pixels on the spatial light modulator incorrectly modulate the wavefront for the instant status of the medium or the whole system. As an example of application, we propose a novel algorithm, the dynamic mutation algorithm, which has high adaptability against perturbations by probing how far the optimization has gone toward the theoretically optimal performance. The diminished focus of scattered light can be effectively recovered when perturbations to the medium cause a significant drop in the focusing performance, which no existing algorithms can achieve due to their inherent strong dependence on previous optimizations. With further improvement, the square rule and the new algorithm may boost or inspire many ap

关键词： scattering modulator algorithm

Learning-based super-resolution interpolation for sub-Nyquist sampled laser speckles

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Photonics Research 2023年第4期11卷 631-642页

作者： huanhao li ZHIPENG YU QI ZHAO YUNQI LUO SHENGFU CHENG TIANTING ZHONG CHI MAN WOO HONGliN liU liHONG V.WANG YUANJIN ZHENG PUXIANG LAI Department of Biomedical Engineering Hong Kong Polytechnic UniversityHong KongChina Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen 518063China School of Electrical and Electronics Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory for Quantum Optics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Caltech Optical Imaging Laboratory Andrew and Peggy Cherng Department of Medical EngineeringCalifornia Institute of TechnologyPasadenaCalifornia 91125USA Photonics Research Institute Hong Kong Polytechnic UniversityHong KongChina

Information retrieval from visually random optical speckle patterns is desired in many scenarios yet considered *** requires accurate understanding or mapping of the multiple scattering process,or reliable capability to reverse or compensate for the scattering-induced phase *** whatever situation,effective resolving and digitization of speckle patterns are ***,on some occasions,to increase the acquisition speed and/or signal-to-noise ratio(SNR),speckles captured by cameras are inevitably sampled in the sub-Nyquist domain via pixel binning(one camera pixel contains multiple speckle grains)due to finite size or limited bandwidth of *** a down-sampling process is irreversible;it undermines the fine structures of speckle grains and hence the encoded information,preventing successful information *** retrace the lost information,super-resolution interpolation for such sub-Nyquist sampled speckles is *** this work,a deep neural network,namely SpkSRNet,is proposed to effectively up sample speckles that are sampled below 1/10 of the Nyquist criterion to well-resolved ones that not only resemble the comprehensive morphology of original speckles(decompose multiple speckle grains from one camera pixel)but also recover the lost complex information(human face in this study)with high fidelity under normal-and low-light conditions,which is impossible with classic interpolation *** successful speckle super-resolution interpolation demonstrations are essentially enabled by the strong implicit correlation among speckle grains,which is non-quantifiable but could be discovered by the well-trained *** further engineering,the proposed learning platform may benefit many scenarios that are physically inaccessible,enabling fast acquisition of speckles with sufficient SNR and opening up new avenues for seeing big and seeing clearly simultaneously in complex scenarios.

关键词： speckle interpolation hence