计算光谱成像技术研究
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摘要
作为近年来刚刚提出的一种新型光谱成像技术,计算光谱成像技术在传统色散型光谱成像技术的基础上,通过在光路中引入适当的编码模板完成目标数据立方体的调制和压缩,然后采用压缩感知理论对探测器获取的二维混叠图像进行三维图谱信息重构,实现了景物空间信息与光谱信息的快照式成像,弥补了传统光谱成像技术光通量低、逐行扫描成像时间长等缺点,极大降低了原始数据量,减轻了数据存储和传输压力。本文对计算光谱成像技术进行了系统性的研究,主要内容包括：
1.归纳总结了传统光谱成像技术的分类、应用及其遇到的瓶颈,介绍了计算光谱成像技术的研究现状。
2.介绍了压缩感知和计算光谱成像技术的基本理论,建立了计算光谱成像系统的数学模型,并对数据反演的关键技术进行了比较分析。
3.分析了编码模板函数和色散元价的选取,对计算光谱成像系统成像链路中各种误差影响因子进行了仿真分析,着重分析了谱线弯曲和谱带弯曲对系统的影响。
4.对计算光谱成像系统提出合理的成像质量评价方法。结合计算光谱成像技术原理和压缩感知重构理论,利用斜边检测技术计算靶标重构图像调制传递函数,并考虑复原光谱的失真度,在此基础上定义了成像质量的评价因子,定量分析了混叠谱段数对系统成像质量的影响。
5.设计研制了计算光谱成像仪实验装置。对系统开展了定标,并进行了成像实验,通过图谱重构,实现了数据立方体的快照式获取。
6.介绍了一种分段连续编码的计算光谱成像技术。利用一次像面分割法将计算光谱成像系统的一次像面分为多个连续的子波段区域,并通过扫描方式获取完整数据立方体的稀疏采样,通过计算机仿真发现重构精度明显提高。
Recently, a novel imaging spectrometry called computational imaging spectrometry has been proposed. Based on the conventional dispersive imaging spectrometry, a coded mask which modulates and compresses the3D spatial-spectral data-cube about the scene is imported appropriately in the light path. The3D spatial-spectral information about a scene of interest is first encoded and captured with one snapshot at the two-dimensional (2D) detector array. Compressed sensing (CS) theory is then used to reconstruct the3D data-cube from the2D aliasing image. Compared to the conventional imaging spectrometry, computational imaging spectrometry eliminates the disadvantages of low light throughput and temporal scanning, greatly reduces the amount of original data, and alleviates the pressure of the data storage and transmission. Systematic study of computational imaging spectrometry is carried out in this paper, and the main work is included as follows:
Firstly, the classification, applications and bottleneck of conventional imaging spectrometers are summarized. The development of computational imaging spectrometry is described.
Secondly, the basic theory of compressive sensing and computational imaging spectrometry is introduced and the mathematic model of computational imaging spectrometry is established. The key technology of data inversion is analyzed comparatively.
Thirdly, the selection of coded mask and dispersive element is introduced. Several kinds of errors are simulated in the imaging chain of computational imaging spectrometry, and the effect of smile and key-stone aberration of the prism on the system is emphatically analyzed.
Fourthly, imaging quality evaluation method of computational imaging spectrometry is proposed. Combined with the principle of computational imaging spectrometry and compressive sensing theory, the slated-edge method is used to calculate the modulation transfer function (MTF) of the reconstructed chart image and the distortion of the recovered spectrum is considered. Then evaluation factor is defined, and the aliasing spectral number, which affects imaging quality of the system, is quantitatively analyzed.
Fifthly, the experimental device is designed and fabricated. The calibration of the system and imaging experiment are carried out. Through spatial-spectral information reconstruction, the capture of data-cube from a snapshot is achieved.
Lastly, a so-called piecewise continuous coding computational imaging spectrometry is described. The first image plane of computational imaging spectrometer is segmented into several continuous spectral sub-band areas. The sparse sampling of the entire spatial-spectral image could be acquired by scanning. The simulation shows the reconstruction accuracy is significantly improved.
1.归纳总结了传统光谱成像技术的分类、应用及其遇到的瓶颈,介绍了计算光谱成像技术的研究现状。
2.介绍了压缩感知和计算光谱成像技术的基本理论,建立了计算光谱成像系统的数学模型,并对数据反演的关键技术进行了比较分析。
3.分析了编码模板函数和色散元价的选取,对计算光谱成像系统成像链路中各种误差影响因子进行了仿真分析,着重分析了谱线弯曲和谱带弯曲对系统的影响。
4.对计算光谱成像系统提出合理的成像质量评价方法。结合计算光谱成像技术原理和压缩感知重构理论,利用斜边检测技术计算靶标重构图像调制传递函数,并考虑复原光谱的失真度,在此基础上定义了成像质量的评价因子,定量分析了混叠谱段数对系统成像质量的影响。
5.设计研制了计算光谱成像仪实验装置。对系统开展了定标,并进行了成像实验,通过图谱重构,实现了数据立方体的快照式获取。
6.介绍了一种分段连续编码的计算光谱成像技术。利用一次像面分割法将计算光谱成像系统的一次像面分为多个连续的子波段区域,并通过扫描方式获取完整数据立方体的稀疏采样,通过计算机仿真发现重构精度明显提高。
Recently, a novel imaging spectrometry called computational imaging spectrometry has been proposed. Based on the conventional dispersive imaging spectrometry, a coded mask which modulates and compresses the3D spatial-spectral data-cube about the scene is imported appropriately in the light path. The3D spatial-spectral information about a scene of interest is first encoded and captured with one snapshot at the two-dimensional (2D) detector array. Compressed sensing (CS) theory is then used to reconstruct the3D data-cube from the2D aliasing image. Compared to the conventional imaging spectrometry, computational imaging spectrometry eliminates the disadvantages of low light throughput and temporal scanning, greatly reduces the amount of original data, and alleviates the pressure of the data storage and transmission. Systematic study of computational imaging spectrometry is carried out in this paper, and the main work is included as follows:
Firstly, the classification, applications and bottleneck of conventional imaging spectrometers are summarized. The development of computational imaging spectrometry is described.
Secondly, the basic theory of compressive sensing and computational imaging spectrometry is introduced and the mathematic model of computational imaging spectrometry is established. The key technology of data inversion is analyzed comparatively.
Thirdly, the selection of coded mask and dispersive element is introduced. Several kinds of errors are simulated in the imaging chain of computational imaging spectrometry, and the effect of smile and key-stone aberration of the prism on the system is emphatically analyzed.
Fourthly, imaging quality evaluation method of computational imaging spectrometry is proposed. Combined with the principle of computational imaging spectrometry and compressive sensing theory, the slated-edge method is used to calculate the modulation transfer function (MTF) of the reconstructed chart image and the distortion of the recovered spectrum is considered. Then evaluation factor is defined, and the aliasing spectral number, which affects imaging quality of the system, is quantitatively analyzed.
Fifthly, the experimental device is designed and fabricated. The calibration of the system and imaging experiment are carried out. Through spatial-spectral information reconstruction, the capture of data-cube from a snapshot is achieved.
Lastly, a so-called piecewise continuous coding computational imaging spectrometry is described. The first image plane of computational imaging spectrometer is segmented into several continuous spectral sub-band areas. The sparse sampling of the entire spatial-spectral image could be acquired by scanning. The simulation shows the reconstruction accuracy is significantly improved.
引文
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