Abstract
The recorded spectra often suffer noise and band overlapping, and deconvolution methods are always used for spectral recovery. However, during the process of spectral recovery, the details cannot always be preserved. To solve this problem, two regularization terms are introduced and proposed. First, the conditions on the regularization term are analyzed for smoothing noise and preserving detail, and according to these conditions, regularization is introduced into the spectral deconvolution model. In view of the deficiency of under noisy condition, adaptive regularization () is proposed. Then semi-blind deconvolution methods based on regularization (SBD-HL) and based on adaptive regularization (SBD-AHL) are proposed, respectively. The simulation experimental results indicate that the proposed SBD-HL and SBD-AHL methods have better recovery, and SBD-AHL is superior to SBD-HL, especially in the noisy case.
© 2015 Optical Society of America
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