本项目研究LAMOST多光纤光谱图像数据处理的理论与方法，旨在突破数据处理的瓶颈，使LAMOST在望远镜硬件系统无法进一步提升的条件下，数据质量和观测效率得以大幅提升。根据系统观测模型，构建LAMOST多光纤光谱图像的成像模型；研究多源信息的盲源信号分离，将不同源的信号及噪声从观测到的多光纤光谱图像中分离出来；研究多光纤光谱图像的像差校正，修正光谱仪的像场弯曲及畸变像差；发展并改进二维盲反卷积抽谱算法，提高抽谱精度；研究基于深度学习的数据挖掘方法，构建适用于低质光谱数据处理的深度学习网络，对LAMOST产生的大量的目前无法使用的低质光谱进行数据挖掘；发展世界领先的二维光谱数据综合处理技术，构建面向LAMOST的二维光谱数据处理应用平台。

This project studies the theory and method of the LAMOST multi-fiber spectrum image data processing, and aims to break through the bottleneck of the data processing and improve the data quality and observation efficiency of LAMOST under the condition that the telescope hardware system couldn't be further improved. Build a complete imaging model of the multi-fiber spectrum image according to the LAMOST systematic observation model; Study blind source separation algorithms, and separate the different sources of signal and noise from the observed multi-fiber spectrum images; Explore the multi-fiber spectrum image aberration correction, and modify the field curvature and distortion aberration of the Spectroscope; Develop and improve the two-dimensional blind deconvolution algorithm and improve the precision of the spectra extraction; Study the data mining based on deep learning, build the deep learning network for low quality spectral data processing, and perform data mining for those low quality useless spectra observed by LAMOST. Develop the world's leading technology for two-dimensional spectral data processing, and build the two-dimensional spectral data processing platform for LAMOST.