Research On Edge Detection Algorithm Based On Wavelet Transformation

Edge is the most basic feature of images, which includes the most part information of images. So obtaining the edge image has turned into a hot spot in research on image processing and analysis technology, So far, many algorithms have been presented in edge detection field. But the problems in anti-noise and edge location were not well resolved because of the complexity and inherent problems of edge detection. The reason is that the noise and edge were both high-frequency signals and it was hardly solved to distinguish between noises and edges from local high-frequency signals using current algorithms."Time-frequency"multi-scale analysis of wavelet transform brings new ways to image edge detection.Wavelet analysis is a new tool of time-frequency analysis after Fourier analysis. It can effectively analyze signal singularity point and detect edges while restraining noise because of its good time-frequency local property and multi-scale characteristics. So, as a powerful tool of researching non-stationary signal, it is paid more attention in the field of information processing and is widely applied in image processing technology.In this paper, the prospect for the development and application of wavelet transformation is introduced firstly and then the research status of image edge detection is given. After studying the characteristics of the classical edge detection algorithm, summing up the advantages and disadvantages of each, wavelet theory applied in edge detection is introduced. According to the evaluation criteria of edge detection, in the considering of the best edge filter design, we identified the criteria for the selection of wavelet bases and choose the fourth-order B-spline wavelet as the wavelet bases through experimental comparison. In the use of wavelet modulus local maxima, for the selection of local modulus maxima, we calculate it along the direction of gradient of the modulus maxima; for threshold selection, we use method of blocking adaptive threshold. Contrast to conventional modulus maxima methods, experiments show that the algorithm in this paper achieved better results at the same time having good noise suppression.