Research On Some Techniques In Edge Detection

Because edge contains a majority of information of image, edge detection is a critical step for image procession and analysis, which has significant influence on the characteristic description, matching and recognition after it. Incorporating the specific project of "Guidance technology based on the matching of optical reference image and infrared real-time image", this thesis concentrates on image edge detection technique research and its application. The main contents include:1. The fundamental theory of edge detection is reviewed, then, the main edge detection methods and some algorithmic evaluation standards are summarized. Based on analyzing the difficulty of edge detection, several problems in current edge detection research are indicated and the edge detection technique's direction is prospected.2. Considering that actual image edges are always diffuse edges, diffuse edge detection is studied. Through analyzing the characteristics of diffuse edges and the difficulty of diffuse edge detection, and then reviewing former diffuse edge detection methods, we smooth out the noise at first, then weaken edges' fuzziness to enhance the edges, so the edges can be similar to step edges, and finally adopt the traditional edge detection method to solve the problems of diffuse edge detection. Therefore, a new image denoising method aimed at removing mixed noise while preserving edges is proposed in the paper based on multilevel median filtering and fuzzy weighted averaging filtering. This algorithm uses fuzzy inference to synthesize the results of multilevel median filter and fuzzy weighted averaging filter, which can better preserve the image edges while smoothing out the mixed noise. As to the aspect of edge enhancement, several typical image enhancement methods are analyzed and compared from the view of reducing the edge fuzziness. Then the shortcoming of morphological edge enhancement algorithm is discussed. Based on it, an adaptive fuzzy enhancement algorithm by edge width reduction is presented. This method can enhance the ramp edges by reducing edge width while smoothing noise out.3. Multiscale edge detection is not only a hotspot of edge detection research but also one of its developmental directions. Because edge denoising has a close relation with edge detection, multiscale adaptive filtering to remove noise and muliscale edge detection is respectively researched. Image denoising based on Wavelet transform has made a good effect for its good time-frequency character, however, the proposed Contourlet transform in recent years, has stronger capability than wavelet transform when describing the edge details. Therefore, an adaptive denoising algorithm based on the Contourlet transform is proposed. This algorithm can do better than traditional wavelet shrinking denoising algorithms in smoothing noise out and preserving edges and details. As to the aspect of multiscale edge detection, by analyzing that the common Laplacian Pyramid (LP) decomposition is not appropriate to capture the edge point singularities, an improved Laplacian Pyramid (LP) decomposition is used, and a multiscale edge detection algorithm based on Laplacian Pyramid is proposed. This algorithm can detect the image edges reliably and effectively.4. Subpixei edge location techniques are researched to improve edge location precision. The origin, principle and prerequisite of subpixei edge location are discussed and the current subpixei edge location methods are summarized. As to two step-edge models of one-dimensional and two-dimensional subpixei location, a subpixei edge location algorithm based on Legendre moments is proposed. The parameters' mathematical expressions of subpixei location based on Legendre moments for one-dimensional and two-dimensional edge are respectively deduced. Because three-gray level edge model can better describe actual image edge than two-gray level edge model, the error because of using two-gray level edge model is analyzed and deduced. Then, effects of Gaussian noise on one-dimensional and two-dimensional subpixei edge location are respectively analyzed. Experiments on emulational images and actual images show that this method can efficiently implement subpixei edge location task.