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

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 

机构地区：[1]Department of Biomedical Engineering,Hong Kong Polytechnic University,Hong Kong,China [2]Hong Kong Polytechnic University Shenzhen Research Institute,Shenzhen 518063,China [3]School of Electrical and Electronics Engineering,Nanyang Technological University,Singapore 639798,Singapore [4]Key Laboratory for Quantum Optics,Shanghai Institute of Optics and Fine Mechanics,Chinese Academy of Sciences,Shanghai 201800,China [5]Caltech Optical Imaging Laboratory,Andrew and Peggy Cherng Department of Medical Engineering,California Institute of Technology,Pasadena,California 91125,USA [6]Photonics Research Institute,Hong Kong Polytechnic University,Hong Kong,China

出　　处：《Photonics Research》2023年第4期631-642,共12页光子学研究（英文版）

基　　金：Agency for Science,Technology and Research(A18A7b0058);Innovation and Technology Commission(GHP/043/19SZ,GHP/044/19GD);Hong Kong Research Grant Council(15217721,C5078-21EF,R5029-19);Guangdong Science and Technology Department(2019A1515011374,2019BT02X105);National Natural Science Foundation of China(81627805,81930048)。

摘　　要：Information retrieval from visually random optical speckle patterns is desired in many scenarios yet considered challenging.It requires accurate understanding or mapping of the multiple scattering process,or reliable capability to reverse or compensate for the scattering-induced phase distortions.In whatever situation,effective resolving and digitization of speckle patterns are necessary.Nevertheless,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 photosensors.Such a down-sampling process is irreversible;it undermines the fine structures of speckle grains and hence the encoded information,preventing successful information extraction.To retrace the lost information,super-resolution interpolation for such sub-Nyquist sampled speckles is needed.In 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 methods.These 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 network.With 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 

分 类 号：TN24[电子电信—物理电子学] TP391.41[自动化与计算机技术—计算机应用技术]