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Neural network-based processing and reconstruction of compromised biophotonic image data

Light(Science & Applications) 2024年第10期13卷 2101-2113页

作者： Michael John Fanous Paloma Casteleiro Costa gagatay lsi Luzhe Huang Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCAUSA Bioengineering Department University of CaliforniaLos AngelesCAUSA California NanoSystems Institute(CNSi) University of CaliforniaLos AngelesCAUSA Department of Surgery David Geffen School of MedicineUniversity of CaliforniaLos AngelesCAUSA

In recent years,the integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging.A compelling trend in this feld involves deliberately compromising certain measurement metrics to engineer better bioimaging tools in terms of e.g,cost,speed,and form-factor,followed by compensating for the resulting defects through the utilization of deep learning models trained on a large amount of ideal,superior or alternative *** strategic approach has found increasing popularity due to its potential to enhance various aspects of biophotonic *** of the primary motivations for employing this strategy is the pursuit of higher temporal resolution or increased imaging speed,critical for capturing fine dynamic biological ***,this approach offers the prospect of simplifying hardware requirements and complexities,thereby making advanced imaging standards more accessible in terms of cost and/or *** article provides an in-depth review of the diverse measurement aspects that researchers intentionally impair in their biophotonic setups,including the point spread function(PSF),signal-to-noise ratio(SNR),sampling density,and pixel *** deliberately compromising these metrics,researchers aim to not only recuperate them through the application of deep learning networks,but also bolster in return other crucial parameters,such as the field of view(FOV),depth of field(DOF),and space-bandwidth product(SBP).Throughout this article,we discuss various biophotonic methods that have successfully employed this strategic *** techniques span a wide range of applications and showcase the versatility and effectiveness of deep learning in the context of compromised biophotonic ***,by offering our perspectives on the exciting future possibilities of this rapidly evolving concept,we hope to motivate our readers from various disciplines to explore novel ways of balancing hardware compromises with compensatio

关键词： offering hardware return

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