ZHU Mengqi, MA Jie, ZHANG Peizhe, SHANG Lunyan, YU Wenkai, ZHANG Anning
Coded aperture snapshot spectral imaging (CASSI) technology enables efficient synergistic acquisition of spatial and spectral information through single-shot compressive imaging. It overcomes the limitations of traditional spectral imaging techniques, which rely on scanning mechanisms and incur high costs on data storage and transmission. This paper systematically reviews the research progress in CASSI technology, focusing on its hardware architecture, theoretical models, and reconstruction algorithms. The hardware design section explores the iterative optimization of system architecture and the impact of coded aperture design on imaging performance. The theoretical model section analyzes the physical modeling methods of single dispersion CASSI and summarizes optimization paths for the theoretical models, highlighting the importance of optical error correction in improving reconstruction accuracy. The reconstruction algorithms section talks about the performance bottlenecks of traditional algorithms introducing recent breakthroughs in deep-learning based reconstruction methods. While deep learning has demonstrated significant advantages in complex scene reconstruction and computational efficiency, challenges related to interpretability, data dependency, and hardware compatibility remain to be addressed. Finally, the paper discusses the future development trends of CASSI technology across multiple dimensions, including system architecture, hardware innovations, algorithm frameworks, and embedded terminal development, aiming to promote its widespread applications in fields such as aerospace remote sensing, biomedicine, deep space exploration, and real-time navigation.
