Hardware implementation of pulse mode RBF neural network-based image denoising. (3rd January 2012)

• Record Type: Journal Article
• Authors: Krid, Mohamed; Gargouri, Amir; Masmoudi, Dorra Sellami
• Is Part Of: International journal of innovative computing and applications. Volume 3:Number 4(2011)
• Page Start: 188
• Page End: 199
• Publication Date: 2012-01-03
• Subjects: hardware implementation -- pulse mode -- radial basis function -- RBF -- neural network -- on-chip learning -- image denoising; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

In this paper, we propose a very compact implementation of a pulse mode radial basis activation function (RBF). The main idea is to make use of the powerful means of RBF neural networks in function approximation and implement a reconfigurable hardware, achieving different image processing tasks with on-chip learning. The proposed network is applied here as illustration in image denoising, which is a very important step in image processing. The efficiency of the proposed pulse mode RBF neural network in image denoising versus other conventional filtering techniques is demonstrated. Moreover, it has been shown that the best strategy, leading to better learning generalisation performances, is to apply in the learning steps all kinds of noise in a random way. In such a way, generalisation is better with respect to each kind of noise. The corresponding design was implemented on a Virtex II PRO FPGA platform and synthesis results are presented.