A Deep Boltzmann machine-based approach for robust image denoising

Pires, Rafael G.; Santos, Daniel S. [UNESP]; Souza, Gustavo B.; Marana, Aparecido N. [UNESP]; Levada, Alexandre L. M.; Papa, João Paulo [UNESP]

A Deep Boltzmann machine-based approach for robust image denoising

Data

2018-01-01

Autores

Pires, Rafael G.

Santos, Daniel S. [UNESP]

Souza, Gustavo B.

Marana, Aparecido N. [UNESP]

Levada, Alexandre L. M.

Papa, João Paulo [UNESP]

Resumo

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.

Como citar

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10657 LNCS, p. 525-533.