Goudail François, Réfrégier Philippe, Delyon Guillaume
Physics and Image Processing Group, Fresnel Institute, Unité Mixte de Recherche 6133, Domaine Universitaire de Saint-Jérôme, 13397 Marseille, France.
J Opt Soc Am A Opt Image Sci Vis. 2004 Jul;21(7):1231-40. doi: 10.1364/josaa.21.001231.
In many imaging applications, the measured optical images are perturbed by strong fluctuations or boise. This can be the case, for example, for coherent-active or low-flux imagery. In such cases, the noise is not Gaussian additive and the definition of a contrast parameter between two regions in the image is not always a straightforward task. We show that for noncorrelated noise, the Bhattacharyya distance can be an efficient candidate for contrast definition when one uses statistical algorithms for detection, location, or segmentation. We demonstrate with numerical simulations that different images with the same Bhattacharyya distance lead to equivalent values of the performance criterion for a large number of probability laws. The Bhattacharyya distance can thus be used to compare different noisy situations and to simplify the analysis and the specification of optical imaging systems.
在许多成像应用中，所测量的光学图像会受到强烈波动或噪声的干扰。例如，对于相干有源成像或低通量成像就是这种情况。在这种情况下，噪声不是高斯加性噪声，并且定义图像中两个区域之间的对比度参数并非总是一项简单的任务。我们表明，对于不相关噪声，当使用统计算法进行检测、定位或分割时，巴氏距离可以是对比度定义的一个有效候选指标。我们通过数值模拟证明，对于大量概率分布，具有相同巴氏距离的不同图像会导致性能准则的等效值。因此，巴氏距离可用于比较不同的噪声情况，并简化光学成像系统的分析和规格说明。
