Variational models for vectorial image denoising involve couplings of different channels to preserve edges

which lead to problems of complexity and efficiency. Meanwhile

different types of couplings result in different edge preserving effects. The objective of our work is to design fast Split-Bregman algorithms for a couple of variational models which have been proposed in recent years and compare their edge preserving properties and their efficiency. Five variational models for vectorial image denoising using different regularizers are studied: the LTV (layered total variation) regularizer

the MTV (multichannel total variation) regularizer

the CTV (color total variation) regularizer

the PA (polyakov action) regularizer

and the RPA (reduced polyakov action) regularizer. The order of their edge preserving quality and computation efficiency are given based on numerical experiments. It is shown that the CTV model is the best method for edge preserving followed by the PA model

the MTV model

the RPA model

and the LTV model. The LTV model is the fastest model

followed by the MTV model

the RPA model

the CTV model

and the PA model.