亚像素多重分形方法在图像处理中的应用

提出了一种用亚像素多重分形原理求取图像奇异性的新型算法,降低了单纯依靠整数像素位置灰度级梯度信息计算边缘测度所产生的误差。该算法结合CCD成像机理给出在亚像素位置的灰度级梯度分布规律,利用多重分形理论将实际图像分割成一系列具有不同奇异性指数的分形集合,对应着从边缘到纹理各层面的图像内容。模拟计算了投影小波中心点改变单位距离对边缘测度的影响程度,得出亚像素分割梯度的方法可以增加计算结果鲁棒性的结论。此方法用于标准图像的分割中,选用5×5亚像素数目提取的最奇异性集合与索贝尔(Sobel)算子(默认阈值为36.7920)提取的边缘的峰值信噪比为9.3981 dB。应用于复杂路面的裂纹提取中,其结果更符合人类的视觉观测。

Sub-Pixel Multifractal Method for Image Processing Application

A new implement algorithm is proposed to acquire the singularities of image based on subpixel multifractal method. The method reduces the errors of the edge measure simply generated by the gray level gradient value at the integer pixel position. The gray pixel level gradient distribution law at the position of subpixel is acquired by the algorithm combining CCD imaging mechanism. Utilizing the multifractal frame, the image is segmented into a series fractal sets of the different singularity exponents, corresponding to the image ranging from the edge to the texture. Moreover, the image edge imformation containing random details can be obtained through the method mentioned above. The influence degree of edge measure is simulated by varying unit distance of wavelet center projection. The results show that the method of subpixel dividing gradient can increase measurement robustness.Comparing most singular manifold extracted from 5×5 subpixel method and edge from Sobel operator (default threshold: 36.7920), the peak signaltonoise ratio is 9.3981 dB, when the algorithm is used in standard image segmentation. Applied on the surface cracks extraction of complex background, the results agree with the human visual reception better.