Application of wavelet transform in edge detection

Abstract

Edge is the most important feature of an image. It contains much of information of an image. In addition, edge detection has a significant influence on the performance of image analysis and comprehension. In order to study the edge detection algorithm on the edge of the location and antinoise capability, we selected Log operator, Canny operator and wavelet transform edge detection algorithm to detect image edge by using Matlab software. Simulation results show that, three edge detection operators possessed different capacity in the edge location and suppress noise. Wavelet transform can provide the edge information of different scales. The experimental result shows that, since wavelet transform have a multi-scale feature and localization characteristic, it can accurately detect the edge points, In the meantime ,it can overcome preferably noisesensitive problems which arise from Log operator and Canny operator .

Evolutionary multiobjective multiple description wavelet based image coding in the presence of mixed noise in images

“…The wavelet transformation methods have been widely used to resolve many image processing problems. For instance, they are used in denoising [26], edge detection [27], feature extraction [28], speech recognition [29], biomedical imaging [30], image compression and image resolution segmentation [31,32] and others [33,34]. Besides this, many MDC generation schemes have been using DWT to create the MDCs [6,13].…”

Comparative Study on Various Edge Detection Techniques for 2-D Image

“…Edges are actually high frequencies in an image, therefore, there is need to use some sort of filter, which separates higher frequencies and lower frequencies. The advantage of using wavelet transform [8] is that it gives out higher and lower frequencies separately, so it is easy to get the edges with this technique. Moreover, the cut-off frequencies of wavelet filters are variable which results in good time and frequency resolution.…”