Robust spike-and-slab deep Boltzmann machines for face denoising

Abstract

The robust Gaussian restricted Boltzmann machine can effectively learn the structure of noise to achieve better results in the face denoising task. The robust Gaussian restricted Boltzmann machine model contains two types of the restricted Boltzmann machine (RBM) model, where a general RBM is used to model the structure of the noise and a Gaussian RBM is used to model the clean data. The spike-and-slab RBM shows better learning abilities than the Gaussian RBM in real images modeling. In addition, the deep Boltzmann machine (DBM) shows powerful image reconstruction ability. To model the real images better, we first stack the spike-and-slab RBM and the RBM to create the spike-and-slab DBM. And then, we utilize the spike-and-slab DBM instead of the Gaussian RBM to model the density of the clean data in the Robust Gaussian RBM, and the proposed method is named as the robust spike-and-slab DBM which can obtain clearer denoising images. Finally, in order to obtain better denoising results, we make use of the learned spike-and-slab DBM model and the mean field method to multi-inference the denoising data learned from the robust spike-and-slab DBM. Experimental results show that the robust spike-and-slab DBM is an effective neural network denoising method.