基于稀疏随机矩阵光场调制的高分辨高速多模光纤三维压缩感知成像（英文）-豆柴文库

您所在位置：网站首页 / 基于稀疏随机矩阵光场调制的高分辨高速多模光纤三维压缩感知成像（英文）.docx / 文档详情

在线预览结束，喜欢就下载吧，查找使用更方便

5 金币

下载文档

/ 2

下载提示

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

文本预览

基于稀疏随机矩阵光场调制的高分辨高速多模光纤三维压缩感知成像（英文）
Title:High-ResolutionHigh-SpeedMulti-ModeFiber3DCompressiveSensingImagingbasedonSparseRandomMatrixLightFieldModulation
Abstract:
Inrecentyears,compressivesensing(CS)hasemergedasapowerfultechniqueforrecoveringhigh-resolutionimageswithsignificantlyreduceddataacquisitionrequirements.Thispaperproposesanovelapproachutilizingsparserandommatrixlightfieldmodulationforhigh-resolution,high-speedmulti-modefiber3Dcompressivesensingimaging.Byexploitingthesparsityofthesignalinthelightfielddomain,theproposedmethodreducesthenumberofmeasurementsrequiredtocapturehigh-quality3Dimages.
1.Introduction
Compressivesensingisarecentlydevelopedsignalprocessingframeworkthatallowsfortherecoveryofsparsesignalsfromalimitednumberofmeasurements.Traditionalimagingsystemstypicallyrequirealargenumberofmeasurementstocapturehigh-resolutionimages,makingthemtime-consumingandresource-intensive.Theproposedapproachaimstoovercometheselimitationsbyleveragingtheuniquepropertiesofsparserandommatrixlightfieldmodulationandmulti-modefibertechnology.
2.LightFieldModulation
Lightfieldimagingcapturesbothspatialandangularinformationofascene,enablingtherecoveryofthree-dimensionaldata.Theproposedmethodutilizesasparserandommatrix-basedmodulationschemetoencodesceneinformationontotheincidentlightfield.Bymodulatingboththeamplitudeandphaseoftheincidentlightfield,theproposedsystemcancapturemoreinformationusingfewermeasurements.
3.Multi-ModeFiberSensing
Multi-modefibershavetheabilitytotransmitmultiplespatialmodessimultaneously,makingthemsuitableforhigh-dimensionaldatatransmission.Inthispaper,weexploitthemulti-modecapabilitiesofopticalfiberstocapturethemodulatedlightfield.Byproperlydesigningthemodulationscheme,wecanachievehigh-resolution3Dimagingwithreduceddataacquisitionrequirements.
4.3DCompressiveSensingImaging
Thecompressivesensingframeworkisappliedtorecovertheoriginalscenefromthecapturedmodulatedlightfield.Byexploitingthesparsityofthesceneinthelightfielddomain,wecanaccuratelyreconstructhi