A parallel linearized ADMM with application to multichannel tgv-based image restoration
A parallel linearized alternating direction method of multipliers (PLADMM) is proposed to solve large-scale imaging inverse problems, which involve the sum of several linear-operator-coupled nonsmooth terms. In the proposed method, the proximity operators of the nonsmooth terms are called individually at each iteration and the auxiliary variables existing in the classical ADMM are excluded. Therefore, the proposed method possesses a highly parallel structure and most of its substeps can be executed simultaneously. The application to multichannel total generalized variation (TGV) based image restoration shows the effectiveness of the proposed method.