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2018 | OriginalPaper | Chapter

A Deep Boltzmann Machine-Based Approach for Robust Image Denoising

Authors : Rafael G. Pires, Daniel S. Santos, Gustavo B. Souza, Aparecido N. Marana, Alexandre L. M. Levada, João Paulo Papa

Published in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Publisher: Springer International Publishing

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Abstract

A Deep Boltzmann Machine (DBM) is composed of a stack of learners called Restricted Boltzmann Machines (RBMs), which correspond to a specific kind of stochastic energy-based networks. In this work, a DBM is applied to a robust image denoising by minimizing the contribution of some of its top nodes, called “noise nodes”, which often get excited when noise pixels are present in the given images. After training the DBM with noise and clean images, the detection and deactivation of the noise nodes allow reconstructing images with great quality, eliminating most of their noise. The results obtained from important public image datasets showed the validity of the proposed approach.

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Footnotes
1
Notice the procedure over the noisy images do not consider the activation field already computed for the clean images.
 
2
In this paper, we evaluated \(T\in [0.1, 0.9]\) with steps of 0.1.
 
3
Since we have \(28\times 28\) images concerning all datasets, the visible layer has \(28\,\times \,28 = 784\) nodes. Also, since we are using Bernoulli-based DBM/DBN models, we employed a min-max normalization of the grayscale values of the images’ pixels.
 
4
Since Semeion database images are \(16\,\times \,16\)-sized, we centered them into a \(28\,\times \,28\)-black-squared window in order to have all images used in the experiments with the very same size.
 
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Metadata
Title
A Deep Boltzmann Machine-Based Approach for Robust Image Denoising
Authors
Rafael G. Pires
Daniel S. Santos
Gustavo B. Souza
Aparecido N. Marana
Alexandre L. M. Levada
João Paulo Papa
Copyright Year
2018
Book
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

Print ISBN: 978-3-319-75192-4

Electronic ISBN: 978-3-319-75193-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75193-1_63

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