光学精密工程2026，Vol.34Issue(1)：1-25,25.DOI:10.37188/OPE.20263401.0001
计算光谱成像系统及光谱重建算法
Computational spectral imaging systems and reconstruction algorithms
摘要
Abstract
Computational spectral imaging,grounded in compressed sensing theory,incorporates optical encoding elements to project high-dimensional spectral image data into low-dimensional measurements,which are subsequently decoded into spectral images using advanced reconstruction algorithms.This para-digm offers notable advantages in system compactness,acquisition speed,and manufacturing cost.In re-cent years,rapid progress has been achieved in both theoretical development and system implementation,resulting in a growing body of high-quality research.Concurrently,consumer-oriented deployments have expanded to platforms such as smartphones,unmanned aerial vehicles,and remote-sensing satellites,en-abling diverse applications in color imaging,environmental monitoring,and medical diagnostics.In this paper,the theoretical foundations and methodological advances of computational spectral imaging are sys-tematically reviewed.Representative optical encoding strategies-including amplitude encoding,wave-length encoding,wavefront encoding,and multi-aperture encoding-are examined,along with mainstream reconstruction approaches ranging from iterative algorithms with prior constraints to end-to-end deep learn-ing models.Finally,emerging trends and key challenges are discussed.Given its strong relevance to stra-tegic emerging industries,including intelligent manufacturing,artificial intelligence,the low-altitude econ-omy,and smart agriculture,computational spectral imaging is expected to play an increasingly important role across a broad range of applications.关键词
计算成像/光谱成像/压缩感知/深度学习Key words
computational imaging/spectral imaging/compressed sensing/deep learning分类
数理科学引用本文复制引用
刘新宇,陈雅婷,吴佳琛,马玉辰,李玉梅,张书赫,郑臻荣,曹良才..计算光谱成像系统及光谱重建算法[J].光学精密工程,2026,34(1):1-25,25.基金项目
国家重点研发计划资助项目(No.2022YFF0705500) （No.2022YFF0705500）
国家自然科学基金资助项目(No.62305183) （No.62305183）
