Markov Chain Monte Carlo sampling algorithms are efficient Bayesian tools to explore complicated posterior distributions. However, sampling in large scale problems remains a challenging task since the Markov chain is very sensitive to the dependencies between the signal samples. In this paper, we are mainly interested in Langevin based MCMC sampling algorithms that allow us to speed up the convergence by controlling the direction of sampling and/or exploiting the correlation structure of the target signal. However, these techniques may sometimes fail to explore efficiently the target space because of poor mixing properties of the chain or the high cost of each iteration. By adding some auxiliary variables, we show that the resulting conditional distribution of the target signal is much simpler to explore by using these algorithms. Experiments performed in the context of multicomponent image restoration illustrate that the proposed approach can achieve substantial performance improvement compared with standard algorithms.