Image Denoising Method based on Threshold, Wavelet Transform and Genetic Algorithm

Abstract

In the process of image acquisition and transmission, noise is always contained inevitably. So it is necessary to image denoising processing to improve the quality of image. Generally speaking, each algorithm has some filtering and threshold parameters. Taking variety kinds of images into account, it is a key problem of how to set these parameters in denoising algorithms under different conditions to achieve better performance. There are many algorithms for the determination of the parameters, and each of them has its application field. Because the wavelet transform has good performance, therefore, it has been widely applied as a kind of signal and image processing tools. In this paper, wavelet transform is used in the image denoising, and the genetic algorithm is used to estimate the denoising results. Experimental results show the validity of the new algorithm.

Image Denoising by Median Filter in Wavelet Domain

“…The ratio of the additive noise in the noisy image content was unknown by the image de-noising method. For this reason, the Robust Median Estimator was use in order to estimate the amount of noise in the image by the equation 5 [1].…”

Image Denoising using Combination of Median Filtering and Wavelet Transform

“…All Discrete wavelet transform [5,[8][9]] is used to find the approximation and detailed coefficients of a discrete signal. It basically represents the time frequency analysis of discrete signal.…”

Biomedical images enhancement based on swarm optimization and differential evolution technique

“…The numerical results show that GA based thresholding techniques are better than Visu shrink, sure shrink and Bayes shrink. In Liu (2015), GA algorithm is used to derive the denoising results. In Korurek et al (2010), GA algorithm is used to evaluate parameter values in a model for a near field effect of X-ray source.…”