A Robust Restricted Boltzmann Machine for Binary Image Denoising

Author(s)

Pires, Rafael ; Levada, Alexandre L. M. ; Souza, Gustavo B. ; Pereira, Luis A. M. ; Santos, Daniel F. S. ; Papa, Joao P. ; IEEE

• Document type: Journal article
• Source: 2017 30TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI); v. N/A, p. 7-pg., 2017-01-01.

Abstract

During the image acquisition process, some level of noise is usually added to the real data mainly due to physical limitations of the acquisition sensor, and also regarding imprecisions during the data transmission and manipulation. Therefore, the resultant image needs to be processed in order to attenuate its noise without loosing details. Machine learning approaches have been successfully used for image denoising. Among such approaches, Restricted Boltzmann Machine (RBM) is one of the most used technique for this purpose. Here, we propose to enhance the RBM performance on image denoising by adding a posterior supervision before its final denoising step. To this purpose, we propose a simple but effective approach that performs a fine-tuning in the RBM model. Experiments on public datasets corrupted by different levels of Gaussian noise support the effectiveness of the proposed approach with respect to some state-of-the-art image denoising approaches. (AU)

• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
• Grantee: João Paulo Papa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 15/09169-3 - Domain adaptation with minimal supervision in multimedia problems
• Grantee: Luis Augusto Martins Pereira
• Support Opportunities: Scholarships in Brazil - Doctorate