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基于深度学习的单像素成像研究进展

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单像素成像通过调制光场测量场景对单个像素探测器的强度响应来还原场景图像,相比依赖阵列探测器捕捉图像信息的传统成像技术,在低成本、宽光谱及特定应用场景下具有出色表现.该技术是一种由物理域转为计算域的新型成像方式,因此众多研究在寻找高效的计算方式.由于神经网络在计算域中的强大学习能力,深度学习技术已经广泛应用于单像素成像中并取得了显著进展.将深度学习单像素成像分为数据驱动式、物理驱动式及混合驱动式,又在每个驱动模式下划分出神经网络用于"图像到图像"和神经网络用于"测量值到图像"两种成像方法.从6种角度综述基于深度学习的单像素成像方法的基本理论和典型案例,并讨论了各类方法的优势与不足.最后对基于深度学习的单像素成像方法进行总结与展望,有前景的应用包括高光谱成像、瞬态观测与目标检测.
Research Progress of Single-Pixel Imaging Based on Deep Learning
Single-pixel imaging reproduces scene images by modulating the light field to measure the intensity response of the scene with a single-pixel detector.Compared with traditional imaging techniques that rely on arrays of detectors to capture image information,single-pixel imaging excels in low-cost,broad-spectrum,and application-specific scenes.This technique is a novel imaging approach that shifts from the physical to the computational domain;hence,many studies are exploring efficient computational approaches.Owing to the powerful learning capability of neural networks in the computational domain,deep learning techniques have been extensively employed in single-pixel imaging and have made remarkable progress.In this paper,deep learning single-pixel imaging is categorized into three modes:data-driven,physical-driven,and hybrid-driven modes.Within each mode,neural networks are further categorized as"image-to-image"and"measurements-to-image"imaging methods.The basic theories and typical cases of single-pixel imaging methods based on deep learning are reviewed from six perspectives,and the advantages and shortcomings of each method are discussed.Finally,single-pixel imaging methods based on deep learning are summarized and discussed,and promising applications include hyperspectral imaging,transient observation,and target detection.

single pixel imagingdeep learningcomputational imagingneural network

王琦、米佳帅

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东北大学信息科学与工程学院,辽宁 沈阳 110819

东北大学流程工业综合自动化国家重点实验室,辽宁 沈阳 110819

河北省微纳精密光学传感与检测技术重点实验室,河北 秦皇岛 066004

单像素成像 深度学习 计算成像 神经网络

国家自然科学基金中央高校基本科研业务费专项辽宁省应用基础研究计划流程工业综合自动化国家重点实验室研究基金河北省自然科学基金山东省自然科学基金山东省自然科学基金沈阳市科技计划

62073068N22040192023JH2/1013001792018ZCX29F2020501040ZR2020MF108ZR2020MD05823-407-3-01

2024

10.3788/LOP232464

激光与光电子学进展
中国科学院上海光学精密机械研究所

激光与光电子学进展

CSTPCD北大核心
影响因子：1.153
ISSN：1006-4125
年,卷(期)：2024.61(10)
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