In image pre-processing, edge detection is a non-trivial task. Sometimes, images are affected by vagueness so that the edges of objects are difficult to distinguish. Hence, the usual edge-detecting operators can give unreliable results, thus necessitating the use of fuzzy procedures. In literature, Chaira and Ray approach is a popular technique for fuzzy edge detection in which fuzzy divergence formulation is exploited. However, this approach does not specify the threshold technique must be applied. Then, in this work, starting from Chairy and Ray procedure, we present a new fuzzy edge detector based on both fuzzy divergence (thought and proved to be a distance) and fuzzy entropy minimization for the thresholding sub-step in gray-scale images. Eddy currents, thermal infrared, and electrospinning images were used to test the proposed procedure after their fuzzification by a suitable adaptive S-shaped fuzzy membership function. Moreover, the fuzziness content of each image has been quantified by new specific indices proposed here and formulated in terms of fuzzy divergence. The results have been evaluated by suitable assessment metrics here formulated and are considered to be encouraging when qualitatively and quantitatively compared with those obtained by some well-known I- and II-order edge detectors.

在图像预处理中，边缘检测是一项艰巨的任务。有时，图像会受到模糊性的影响，因此难以区分对象的边缘。因此，通常的边缘检测算子会给出不可靠的结果，因此需要使用模糊过程。在文献中，Chaira和Ray方法是一种流行的模糊边缘检测技术，其中利用了模糊散度公式。但是，此方法未指定必须应用阈值技术。然后，在这项工作中，从Chairy和Ray程序开始，我们针对灰度图像中的阈值子步，提出了一种基于模糊散度（被认为是距离）和模糊熵最小化的新型模糊边缘检测器。涡流，热红外，静电纺丝图像通过合适的自适应S形模糊隶属度函数用于模糊化后测试所提出的程序。此外，每个图像的模糊度内容已通过此处提出的新特定指标进行了量化，并根据模糊散度进行了公式化。结果已通过此处制定的适当评估指标进行了评估，并且与某些知名的I级和II级边缘检测器获得的结果相比，定性和定量分析认为这是令人鼓舞的。