  Image denoising based on a probabilistic model of local image patches has
been employed by various researchers, and recently a deep (denoising)
autoencoder has been proposed by Burger et al. [2012] and Xie et al. [2012] as
a good model for this. In this paper, we propose that another popular family of
models in the field of deep learning, called Boltzmann machines, can perform
image denoising as well as, or in certain cases of high level of noise, better
than denoising autoencoders. We empirically evaluate the two models on three
different sets of images with different types and levels of noise. Throughout
the experiments we also examine the effect of the depth of the models. The
experiments confirmed our claim and revealed that the performance can be
improved by adding more hidden layers, especially when the level of noise is
high.
