Deconvolution methods based on convex regularization for spectral resolution enhancement

Spectral resolution enhancement is essential for spectral analysis and assignment. In this study, a regularization term in form of a convex function φHS is included in the spectral deconvolution model to enhance spectral resolution. Based on the regularization term, a non-blind deconvolution (NBD) method is proposed, and to improve feasibility in practice, a semi-blind deconvolution (SBD) method is also presented. Simulation and experimental results demonstrate that both methods enhance spectral resolution effectively. When the blur kernel is known accurately, NBD achieves better performance than SBD. In other cases, the latter achieves better results.