Abstract: Two-dimensional spectral images are generally distorted. Spectrum extraction operation is affected by such distortion, which reduces the quality of one-dimensional spectral data. Aiming at above problem, an effective correction method for the distorted two-dimensional spectral images based on neural network is proposed. Firstly, by extracting the center line of each fiber from the flat-field spectrum and fitting the equal-wavelength line at each specific wavelength from the calibration lamp spectrum, data that represent distortion characteristics from two-dimensional spectral images can be obtained. The training samples are thus constructed according to these two sets of feature lines. Secondly, a neural network model is then designed and trained to fit the relation between the pixel coordinates of the image before and after correction. Therefore, all pixel coordinate values of the corrected image can be calculated by the model. Finally, the flux values of the corrected image are filled one-to-one in accord with the flux value of the original distorted image. The correction experiments are carried out with the flat-field spectrum, calibration lamp spectrum, and object spectrum respectively. The spectral extraction results of the object spectrum before and after correction are compared. Experimental results prove that the method can correct the distorted two-dimensional spectral image effectively and improve quality of one-dimensional spectral data to an extent.

Key words: two-dimensional spectra, neural network, image processing, image correction

摘要： 二维光谱图像普遍存在畸变现象，这种畸变会影响抽谱操作，从而降低一维光谱数据的质量。针对上述问题，提出了一种有效的基于神经网络的二维光谱图像的校正方法。该方法首先从二维光谱图像中提取表示畸变特征的数据，即抽取平场光谱中每条光纤的中心线以及拟合定标灯谱中每个特定波长处的特征谱线，再根据这两组线构造数据集；然后设计神经网络模型，训练模型拟合图像校正前后的像素坐标值之间的关系，通过模型计算出校正图像的所有像素坐标值；最后根据原始畸变图像的通量值一一对应填充校正图像的通量值。分别用平场光谱、定标灯谱、目标光谱数据进行校正实验。与此同时，对比了目标光谱图像校正前后的抽谱结果。实验证明该方法能够有效校正弯曲的二维光纤光谱图像，并在一定程度上提升一维光谱数据的质量。

关键词: 二维光谱, 神经网络, 图像处理, 图像校正