  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.
