Statistical Neural Networks Based Blind Deconvolution of Spectroscopic Data

The resolution of the spectroscopic data can be improved by mathematically removing the degraded effect of the instrument response function. Based on the Shalvi-Weinstein criterion, a statistical neural networks based blind deconvolution algorithm for spectroscopic data is proposed. The true spectra and the spectral slit functions of measure instruments can be estimated simultaneously. Especially, the pre-whitening processing can result in a robust convergence, and the noise can be reduced considerably meanwhile.