An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network

Kai Hu,Min Xia,Aiguo Song,Xiaoling Ye,Yanyan Dou

doi:10.1155/2013/573941

Abstract

A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA) to decide weights in a back propagation neural network (BPN). It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.

Highlights

Digital images are frequently corrupted by impulse noise during image gaining or transmission
This paper proposes making use of gene algorithm to improve backpropagation neural networks to find the most suitable network connection weights and network, form a genetic algorithm (GA)-BP model and apply it to image filtering
We used an adaptive weighted average algorithm which varies for windows size and distance to recover noise pixels which are recognized by GA-back propagation neural network (BPN)

Summary

Introduction

Digital images are frequently corrupted by impulse noise during image gaining or transmission. The SM filter works effectively for low noise densities but at the cost of blurring the image One solution to this problem is the weighted median (WM) filter [2] which gives more weight to some values within the window than others. The special case of the WM filter is the centre weighted median (CWM) [3] filter which gives more weight only to the centre value of the window These filters do not perform well at higher noise densities. Each pixel of the image is considered to be noisy which may not be in practice These filters do not detect whether a pixel in the image is corrupted by impulse or not and replace each pixel by the median value.

Related Theory

Our Work

Experiment Data

Findings

Conclusions