改进的模糊推理规则图像边缘检测算法

图像边缘检测是数字图像处理领域的关键技术，边缘检测的结果决定了图像后续处理的质量。模糊推理规则边缘检测算法具有较强的边缘检测能力，并且具备一定的抗噪效果。但是，这种算法只在高斯噪声较小时有效，当高斯噪声较大时它的边缘检测效果甚至比Canny等算子的效果还差。针对模糊推理规则算法在强高斯噪声时效果较差的问题，提出一种改进的模糊边缘检测算法。该算法能够根据图像含噪情况调整边缘检测方案：当噪声较弱时，使用模糊推理规则边缘检测算法；当噪声较强时，为提高算法抑制噪声的能力，使用改进的模糊推理规则边缘检测算法。实验结果表明，该方法具有更好的抗噪性能和边缘检测能力。

Improved image edge detection algorithm based on fuzzy inference rules

Image edge detection is a key technology in the field of digital image processing. The result of edge detection determines the quality of subsequent processing. Fuzzy inference rules detection algorithm is an efficient edge detection algorithm with some anti-noise effect. However, this algorithm performs well only when Gaussian noise is weak. Its edge detection effect is even worse than the Canny operator when the Gaussian noise is strong. In this paper, an improved image fuzzy edge detection algorithm is proposed to solve this problem. This algorithm can adjust noise edge detection scheme according to the noise intensity. It uses fuzzy inference rules detection algorithm when the noise is weak and uses the improved fuzzy inference rules detection algorithm to improve the effect of anti-noise when the noise is strong. Experimental results show that, the proposed algorithm has better anti-noise performance and edge detection ability.