Distortion Correction of Two-Dimensional Spectral Image Based on Neural Network

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.

Keywords

• two-dimensional spectra; neural network; image processing; image correction