早期压缩感知在光学成像领域的应用主要集中在空域压缩成像。近年来,更多的空域压缩成像采用阵列式探测器取代单元探测器采集测量值。同时,压缩成像的研究也从二维空间拓展到三维测距、高速成像、多光谱成像、关联成像和全息成像等方向。本文针对空域高分辨率压缩成像、压缩感知测距和时域高速压缩成像进行详细分析,结合空域压缩成像总结了测量矩阵设计的研究进展,讨论研究中遇到的困难以及未来可能发展的机遇,并对压缩感知在多光谱、关联成像、和全息成像中的应用研究进行了讨论。此外,本文也总结了近几年深度学习技术在各应用方向上对系统目标恢复性能的改善。

The early application of compressive sensing in optical imaging focuses on spatial compressive imaging. In recent years, increasing compressive imaging systems have employed detector array instead of a single detector for collecting measured values. Moreover, the scope of compressive imaging expands from two-dimensional space to three-dimensional ranging, high-speed imaging, multispectral imaging, ghost imaging, and holography imaging. Herein, we analyzed recent works on high-resolution compressive imaging, compressive sensing ranging, and temporal high-speed compressive imaging with details, summarized the research progresses of measured matrix design by combining spatial compressive imaging, works on sensing matrix design in spatial compressive imaging, discussed their challenges and future development opportunities, and reviewed the applications of compressive sensing in multispectral imaging, ghost imaging, and holography imaging. Furthermore, we summarized the improvement of reconstruction performance of system targets by applying deep learning to compressive imaging.