采用传统图像检测方法存在目标区域定位不准确、目标细节信息丢失、目标形状变形等问题,本文提出一种基于离散分数布朗随机场模型的水下图像目标检测方法。该方法根据分形理论和水下图像的特点,以图像中每个像素点为中心取窗口,计算在该窗口内的分形维数均值,将该均值作为中心像素的分形特征,然后根据分形维数分布图确定分割阈值,从而实现对水下图像分割,并且通过将目标表面不同尺度下的灰度差分平均值进行归一化处理,减少了用于表示不同尺度下的平均绝对值灰度差分的数据,从而提高算法检测效率。实验结果表明,该方法对水下成像条件具有一定鲁棒性,是一种有效的水下图像目标检测方法。

To overcome the shortcomings of traditional methods, a method of underwater image segmentation based on the discrete fractional Brownian random field was proposed to dispose underwater images. At first, a window was set up, and the centre of window was located at the position of each pixel in the image. The average of fractal dimension in the window was calculated, and it was considered as the fractal feature of the pixel at the centre of window. At last, a threshold was determined according to the graph of fractal dimension, and the segmentation of underwater image was completed. By the normalization of the average absolute intensity difference on surfaces at difference scales, the number of data items used to represent the average absolute intensity difference on surfaces at difference scales was reduced, and the segmentation efficiency was improved. Finally, the results on some typical images were presented. Compared with the results obtained by the segmentation methods of Ostu and Maximum Entropy, it shows that the presented method is robust and efficient in underwater image segmentation.