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.  and Xie et al.  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.
|Publication status||Published - 1 Jan 2013|
|MoE publication type||Not Eligible|
|Event||International Conference on Learning Representations - Scottsdale, United States|
Duration: 2 May 2013 → 4 May 2013
Conference number: 1
|Conference||International Conference on Learning Representations|
|Period||02/05/2013 → 04/05/2013|