A RESOLUTION ENHANCING ALGORITHM FOR GAMMA-RAY SPECTRUM BASED ON BLIND DECONVOLUTION AND LP-NORM SPARSITY CONSTRAINT

摘要

Current gamma-ray spectrum analysis method uses a preset system response matrix to improve the resolution of gamma-ray spectrum. However, the system response matrix may not be available or biased due to limitation of experiment conditions, which can degrade the accuracy of gamma-ray spectrum analysis. To solve the problem, a new reconstruction method based on blind deconvolution and sparsity constraint is proposed to improve the resolution of gamma-ray spectrum in this study. The proposed method models the modulation of spectrometer as a convolution operation and reconstructs the high resolution spectrum as well as the convolution kernel simultaneously. Lp-norm based sparsity constraint is imposed to stabilize the demodulation of spectrometer and reduce the background oscillations, so that the resolution can be enhanced. The results of both numerical simulation and experiments demonstrate that the proposed method can effectively improve the resolution of gamma-ray spectrum and reduce background oscillations without any aid of system response matrix. Keywords: Gamma-ray spectrum analysis, blind deconvolution, Lp-norm, sparsity