Department of Electrical and Computer Engineering,University of Calgary, Calgary, AB, Canada.
IEEE Trans Image Process. 2002;11(9):1062-71. doi: 10.1109/TIP.2002.802526.
New methods for detecting edges in an image using spatial and scale-space domains are proposed. A priori knowledge about geometrical characteristics of edges is used to assign a probability factor to the chance of any pixel being on an edge. An improved double thresholding technique is introduced for spatial domain filtering. Probabilities that pixels belong to a given edge are assigned based on pixel similarity across gradient amplitudes, gradient phases and edge connectivity. The scale-space approach uses dynamic range compression to allow wavelet correlation over a wider range of scales. A probabilistic formulation is used to combine the results obtained from filtering in each domain to provide a final edge probability image which has the advantages of both spatial and scale-space domain methods. Decomposing this edge probability image with the same wavelet as the original image permits the generation of adaptive filters that can recognize the characteristics of the edges in all wavelet detail and approximation images regardless of scale. These matched filters permit significant reduction in image noise without contributing to edge distortion. The spatially adaptive wavelet noise-filtering algorithm is qualitatively and quantitatively compared to a frequency domain and two wavelet based noise suppression algorithms using both natural and computer generated noisy images.
提出了一种利用空间域和尺度空间域检测图像边缘的新方法。利用关于边缘几何特征的先验知识，为任何像素位于边缘的可能性分配一个概率因子。为空间域滤波引入了一种改进的双阈值技术。基于像素在梯度幅度、梯度相位和边缘连接上的相似性，为像素属于给定边缘的概率分配。尺度空间方法使用动态范围压缩允许在更大的尺度范围内进行小波相关。使用概率公式将在每个域中过滤获得的结果组合起来，提供最终的边缘概率图像，该图像具有空间域和尺度空间域方法的优点。使用与原始图像相同的小波对该边缘概率图像进行分解，允许生成自适应滤波器，可以识别所有小波细节和近似图像中边缘的特征，而与尺度无关。这些匹配滤波器可以在不导致边缘变形的情况下显著降低图像噪声。使用自然和计算机生成的噪声图像，对空间自适应小波噪声滤波算法进行了定性和定量比较，与频域和两种基于小波的噪声抑制算法进行了比较。
