Study On Fuzzy Image Edge Detection Based On Wavelet Denoising Preprocessing

Image edge detection is an important aspect of image processing. Many methods are proposed by many researchers. The mainly research of this paper is the study on image denoising and edge detection based on wavelet transform and fuzzy theory.Many images have noise in actual application. Noise effects the following image operations, such as image analysis and image compression. So image denoising is necessary before the edge detectioa According to the multi-resolution analysis of wavelet transform, an adaptive wavelet threshold denoising algorithm is presented for noise removal of the image. Because different relativity of different detail subimages in region and direction, this method estimates wavelet coefficients' variances by using different window in different detail subimages. Then the wavelet coefficients are processed by an improve threshold and reconstructed. The experiment result shows that the algorithm can maintain the details of the image while remove the noise effectively.Fuzzy technology is fit for using in image enhancement, image segment and edge detection, because the fuzzy characteristics of human vision and images. GA has better global searching ability, because it can process several individuals and estimate the result in the same time. This paper proposed a new method of image fuzzy edge detection based on genetic algorithm. First, gradient image is constructed by using direction templates convolve with image. Then, fuzzy partition to gradient image is implemented. To solve the difficulty of selecting the size of fuzzy window, a range of it is presented according to the gradient image. Genetic algorithm is used for searching the best threshold and reducing the running time. The edge detection is realized according to the segmentation of gradient image by the threshold. Experiment result shows that the proposed method has better performance on edge detection and computing speed.