Due to limited size and imperfect of the optical components in a spectrometer, aberration has inevitably been brought into two-dimensional multi-fiber spectrum image in LAMOST, which leads to obvious spacial variation of the point spread functions (PSFs). Consequently, if spatial variant PSFs are estimated directly , the huge storage and intensive computation requirements result in deconvolutional spectral extraction method become intractable. In this paper, we proposed a novel method to solve the problem of spatial variation PSF through image aberration correction. When CCD image aberration is corrected, PSF, the convolution kernel, can be approximated by one spatial invariant PSF only. Specifically, machine learning techniques are adopted to calibrate distorted spectral image, including Total Least Squares (TLS) algorithm, intelligent sampling method, multi-layer feed-forward neural networks. The calibration experiments on the LAMOST CCD images show that the calibration effect of proposed method is effectible. At the same time, the spectrum extraction results before and after calibration are compared, results show the characteristics of the extracted one-dimensional waveform are more close to an ideal optics system, and the PSF of the corrected object spectrum image estimated by the blind deconvolution method is nearly central symmetry, which indicates that our proposed method can significantly reduce the complexity of spectrum extraction and improve extraction accuracy.

中文翻译：

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基于机器学习技术的二维多光纤光谱图像校正

由于光谱仪中光学元件的尺寸有限和不完善，LAMOST中的二维多光纤光谱图像不可避免地会带来像差，从而导致点扩散函数（PSF）的明显空间变化。因此，如果直接估计空间变体 PSF，巨大的存储和密集的计算需求导致解卷积光谱提取方法变得难以处理。在本文中，我们提出了一种通过图像像差校正解决空间变化 PSF 问题的新方法。当校正CCD图像像差时，卷积核PSF只能用一个空间不变PSF来近似。具体而言，采用机器学习技术校准失真光谱图像，包括总最小二乘法（TLS）算法，智能采样方法，多层前馈神经网络。LAMOST CCD图像的标定实验表明，该方法的标定效果是有效的。同时，对比标定前后的光谱提取结果，结果表明提取的一维波形特征更接近理想光学系统，盲反卷积估计的校正后物体光谱图像的PSF方法几乎是中心对称的，这表明我们提出的方法可以显着降低频谱提取的复杂度并提高提取精度。