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Hardware implementation of Neural-Fuzzy Network based image denoising approximation

In this paper, we propose a new architecture of Neural-Fuzzy Network (NFN) devoted to function approximation tasks. NFN with on chip learning offers the possibility of reconfiguration and the generality of the solution since it can approximate any input-output function through parameters update. Back-propagation learning algorithm constitutes an appropriate method that can make an efficient approximation of NFN parameters. In this context, the main idea is to implement the proposed NFN based on the back-propagation algorithm using Field Programmable Gate Arrays (FPGA). However, the complexity of such system, presents a drawback for hardware implementation. Therefore, we make use of pulse mode since it can support this problem thanks to its higher density of integration. To verify the proposed design performance, we consider image denoising function approximation as illustration example. Experimental results reveal the performance and efficiency of the proposed NFN versus other conventional filtering techniques. Synthesis results on a FPGA platform are presented and discussed.