Researching Of Self-Adaptable Wavelet Transform Arithmetic In The Image Edge Detection

Edge is the basic feature of images, which includes the most part information of images. According to theory on computer vision, image edge detection occupies an important position in computer image research. Research on the problem in image detection, however, is very difficult because of the complexity of the problem and limitation of the available technique. So far, many algorithms have been presented in edge detection field, but the problems in anti-noise and edge location were not well resolved. it is hardly solved to distinguish between noises and edges from local high-frequency signals using current algorithms.Multiresolution analysis is developed from the consonance analysis. Wavelet transform is a new tool of time-frequency analysis after Fourier transform. 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. Wavelet transform has been a powerful tool of researching non-stationary signal, and it is paid more attention in the field of information processing and is widely applied in image processing technology.Entropy is measurement which describes the state of motion or the uncertainty of existence. Source sends out message while the message loads the information. The smaller the probability of a message appears, the greater the uncertainty. In other words, the greater the amount of information. Instead, the greater the probability of message occurrence, the less the uncertainty is. In other words, the amount of information is small. Entropy reflected in the image is the local image entropy which can be characterized by the number of edge elements. Local entropy can be regard as the judgment which represents the amount of edge elements of an area in a image.In this paper, the course of 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 algorithm, wavelet theory applied in edge detection is introduced.Compared with traditional methods of edge detection, the method based on wavelet transform improves the edge localization precision avoiding noise effectively. The scale, however, impacts the effect of output image. In small scale, edges can be localized accurately, but are sensitive to noise, while in large scale, noise can be suppressed effectively, but edges may deviate from original location. A self-adaptable scale selection method with local entropy is given in this paper. The local entropy which can divide image precisely is adopted to preprocess the image. The self-adaptable scale selection method of wavelet transform detects the edge in corresponding scale. Experimental results show that the proposed method provides clearer and more accurate edges than traditional self-adaptable arithmetic based on wavelet transform...