计算光谱成像系统及光谱重建算法OA

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.