摘要
光谱成像旨在获取目标场景空间-光谱三维数据立方体,从而显著提高对目标的识别和分类能力,广泛应用于军事和民用等多个领域.传统光谱成像技术多基于奈奎斯特采样理论构建,受制于三维数据立方体和二维传感器阵列之间的矛盾,难以兼顾空间、光谱和时间分辨率.新型计算光谱成像基于压缩感知理论体系,首先通过光学系统对三维数据立方体进行光场编码压缩投影,然后通过光谱重建算法实现三维数据立方体解码,可兼顾空间、光谱和时间分辨率.本文从统一的计算光谱成像理论出发,系统性梳理了光场编码的3种方式:像面编码、点扩散函数编码和光谱响应编码.同时,探讨了两种算法解码方式:基于物理模型与先验知识、基于深度学习的端到端重建两类算法.并讨论了各类方法之间的区别与联系,分析了各自的优缺点.最后对计算光谱成像技术的未来发展趋势及研究方向进行了展望.
Abstract
Spectral imaging aims to obtain three-dimensional spatial-spectral data cubes of target scenes that substantially improves the recognition and classification capabilities of targets.It has been widely used in various fields,including military and civilian applications.Traditional spectral imaging techniques are mostly based on the Nyquist sampling theory.However,these techniques face challenges in balancing spatial,spectral,and temporal resolutions due to limitations posed by two-dimensional sensor arrays when capturing three-dimensional data cubes.The computational spectral imaging is based on the compressed sensing theory system.First,the optical system is used to encode and compress the projection of the three-dimensional data cube.Then,a spectral reconstruction algorithm is used to decode the three-dimensional data cube,which can balance spatial,spectral,and temporal resolutions.Starting from the unified theory of computational spectral imaging,this paper systematically summarizes three methods of optical field encoding:image plane,point spread function,and spectral response encoding.Additionally,it explores two types of algorithmic decoding:one is based on physical models and prior knowledge,while the other is based on deep learning for end-to-end reconstruction.Furthermore,this paper discusses the differences and connections between these methods,analyzing their respective advantages and disadvantages.Finally,it explores future development trends and research directions of computational spectral imaging technology.
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