Application Of Wavelet On Image Edge Detection And Denoising

Image edge contains most of the important information of an image, detection of edge plays an important role in image processing and machine vision. Noise is added to the image during the acquirement,transmission and deposit for many reasons. So how to improve the quality of the image becomes a very important task in image processing. Wavelet is an useful kind of analytic tool. Along with the improvement and perfection of the theory of wavelet in recent years, it has been applied on almost all the embranchments of image processing, such as image denoising, edge detection, image compression and image division etc. This article mainly studies the application of wavelet on edge detection and image denoising.This article systemically introduces and summarizes the theory of wavelet. Continuous Wavelet Transform, Discrete Wavelet Transform, Multi-Resolution Analysis, Wavelet Bases and Dyadic Wavelet Transform are introduced. The fast algorithm of Discrete Dyadic Wavelet Transform is also given.The classic methods of edge-detection are introduced and analyzed. Algorithm of edge detection by modulus maximums based on Mallat and Trous is deeply studied. Based on the above analyses, a simplification modulus maximums edge detection algorithm based on Trous which reduces the complexity greatly is proposed. And it also studies the application of Mathematical Morphology on edge detection and proposes some improvements on the morphology edge examination operator and structure element. Then it presents a new edge detection algorithm combines wavelet and morphology together, which is proved to be efficient on edge precision, detail edge detection and restraining noise by experiments.Traditional methods for denoising will result in dim edge while taking out the noise. This article proposes a method on image denoising based on edge detection which divides the image into"edge area"and"noise area"after edge detection. Apply different method for different area, then combine the two kinds of modulus together to achieve the gain that protect the edge while denoising.