Boltzmann machines for image denoising

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationArtificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings
Pages611-618
Number of pages8
DOIs
StatePublished - 2013
Event23rd International Conference on Artificial Neural Networks, ICANN 2013 - Sofia, Bulgaria
Duration: Sep 10 2013Sep 13 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8131 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other23rd International Conference on Artificial Neural Networks, ICANN 2013
CountryBulgaria
CitySofia
Period9/10/139/13/13

Keywords

  • Deep Boltzmann Machine
  • Deep Learning
  • Image Denoising
  • Restricted Boltzmann Machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Cho, K. (2013). Boltzmann machines for image denoising. In Artificial Neural Networks and Machine Learning, ICANN 2013 - 23rd International Conference on Artificial Neural Networks, Proceedings (pp. 611-618). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8131 LNCS). https://doi.org/10.1007/978-3-642-40728-4_76