A new restricted boltzmann machine training algorithm for image restoration

A variety of approaches have been proposed for addressing different image restoration challenges. Recently, deep generative models were one of the mostly used ones. In this paper, a new Restricted Boltzmann Machines (RBM) training algorithm for addressing corrupted data has been proposed. RBMs can be trained both supervised and unsupervised, however they are very sensitive to noise and occlusion. The proposed algorithm enables the RBM to be robust against corruptions. Using the new algorithm, we have given the RBM a posterior knowledge about desired or clean data. Despite other methods, the proposed algorithm works fine without changing the architecture of the model or adding any regularization term. Concretely, the RBM can be used as a robust feature extractor, even for unclean data. By creating different corrupted versions for each image instance, and using the original version in the reconstruction phase, the RBM can learn the desired probability distribution of data. Experimental results confirm the robustness of the model against different types of corruption.