Boltzmann Machines for Image Denoising

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 in [2] and [17] 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 these two models on three different sets of images with different types and levels of noise. The experiments confirmed our claim and revealed that the denoising performance can be improved by adding more hidden layers, especially when the level of noise is high.