МЕТОД ВЫДЕЛЕНИЯ ГРАНИЦ НА ИЗОБРАЖЕНИЯХ НА ОСНОВЕ ВЕЙВЛЕТ-ПРЕОБРАЗОВАНИЯ
Authors: Шлеймович М.П. 
Published: February 2025

В работе описан подход к выделению границ на основе весовой модели изображений с применением вейвлет-преобразования. В рамках данной модели вычисляются веса пикселей, которые представляют собой оценки их значимости с точки зрения вклада в полную энергию изображения. Для выделения границ определяются наиболее значимые пиксели, веса которых превышают заданное пороговое значение. Показано, что предложенный метод обладает относительно высокими показателями устойчивости к шуму и скорости работы.
Introduction. There are a lot of methods for segmenting images to detect their edges. These methods have different speed and noise sensitivity. Therefore, despite the large amount of existing methods, the problem of developing and studying new approaches aiming at detecting image edges is topical and has practical significance. The aim of the research was to develop a new method for detecting image edges ensuring high resistance to interference and processing speed. Research technique. Math of the wavelet transform is widely used for image analysis. Wavelet transform allows to represent the analyzed image as a set of copies of different resolution (scale), highlighting its features. Large-scale orthogonal wavelet transform represents the image as a set of numerical matrices, one contains the approximation coefficients, and the other ones - detail coefficients. Considering the dependencies between the pixels of the image copies at different levels, a weighting model is built. Here, each pixel of the original image is estimated with the use of a certain weight - a measure of pixel significance. One can estimate the significance of pixels in terms of their contribution to the total energy of the image. At the same time, it is assumed that the most significant pixels are the ones that exhibit the features of the image including the changes in intensity at the edges. To detect the image edges, one can perform the following actions: smooth the original image build a weighting model of the image convert the weighting model into a weighted image (the values of the weights are reduced to the range of intensity values of the original image) smooth the weighting image set a threshold weight value carry out threshold conversion of the weighted image. Findings. The implementation of the method is a noise-resistant and high-speed edge detector. Studies showed that the average error in detecting edges in normally distributed noise with a standard deviation of less than 10 of the maximum intensity value did not exceed 38 according to the standard deviation criterion and 15 according to the Kullback-Leibler criterion. It is also shown that the average operating time of the software developed with the use of the Microsoft Visual Studio 2017 framework on the computer with Microsoft Windows 10 operating system, 4 AMD A8-7410 APU processors, AMD Radeon R5 Graphis and 8 GB RAM, is roughly 50 ms. Practical significance. The proposed method of detecting image edges can be used in the information processing and control systems based on computer vision technologies. The method has a relatively high resistance to interferences and high processing speed.