  Bolton and Schlegel presented a promising deconvolution method to extract 1D
spectra from a 2D optical fiber spectral CCD image. The method could eliminate
the PSF difference between fibers, extract spectra to the photo noise level, as
well as improve the resolution. But the method is limited by its huge
computation requirement and thus cannot be implemented in actual data
reduction. In this article, we develop a practical computation method to solve
the computation problem. The new computation method can deconvolve a 2D fiber
spectral image of any size with actual PSFs, which may vary with positions. Our
method does not require large amounts of memory and can extract a 4k multi 4k
noise-free CCD image with 250 fibers in 2 hr. To make our method more
practical, we further consider the influence of noise, which is thought to be
an intrinsic illposed problem in deconvolution algorithms. We modify our method
with a Tikhonov regularization item to depress the method induced noise.
Compared with the results of traditional extraction methods, our method has the
least residual and influence by cross talk and noise, even for extreme
situation. Our method can convergent in 2 to 4 iterations, and the computation
times are about 3.5 hr for the extreme fiber distance and about 2 hr for
nonextreme cases. Finally, we apply our method to real LAMOST (Large sky Area
Multi-Object fiber Spectroscopic Telescope, a.k.a. Guo Shou Jing Telescope)
data. We find that the 1D spectra extracted by our method have both higher
signal-to-noise ratio and resolution than the traditional methods.
