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Fusion-based enhancement of multi-exposure Fourier ptychographic microscopy 认领 引用 被引量：2
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作者 Zhiping Wang Tianci Feng +2 位作者 Aiye Wang Jinghao Xu An Pan 《Advanced Photonics Nexus》 CSCD 2025年第4期1-11,共11页
Fourier ptychographic microscopy(FPM)is an innovative computational microscopy approach that enables high-throughput imaging with high resolution,wide field of view,and quantitative phase imaging(QPI)by simultaneously... Fourier ptychographic microscopy(FPM)is an innovative computational microscopy approach that enables high-throughput imaging with high resolution,wide field of view,and quantitative phase imaging(QPI)by simultaneously capturing bright-field and dark-field images.However,effectively utilizing dark-field intensity images,including both normally exposed and overexposed data,which contain valuable high-angle illumination information,remains a complex challenge.Successfully extracting and applying this information could significantly enhance phase reconstruction,benefiting processes such as virtual staining and QPI imaging.To address this,we introduce a multi-exposure image fusion(MEIF)framework that optimizes dark-field information by incorporating it into the FPM preprocessing workflow.MEIF increases the data available for reconstruction without requiring changes to the optical setup.We evaluate the framework using both feature-domain and traditional FPM,demonstrating that it achieves substantial improvements in intensity resolution and phase information for biological samples that exceed the performance of conventional high dynamic range(HDR)methods.This image preprocessing-based information-maximization strategy fully leverages existing datasets and offers promising potential to drive advancements in fields such as microscopy,remote sensing,and crystallography. 展开更多
关键词 Fourier ptychographic microscopy multi-exposure image fusion computational imaging feature-domain nonlinear image fusion
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