基于优化导向滤波的模糊图像特征提取仿真

Simulation of Fuzzy Image Feature Extraction Based on Optimized Guided Filtering

目前的图像特征提取方法在对模糊图像特征进行提取时,没有通过导向滤波对模糊图像进行优化,导致模糊图像特征提取结果不能有效保留图像边缘细节信息、提取图像特征运行速度较慢以及图像特征提取效果较差。提出基于优化导向滤波的模糊图像特征提取方法。首先对模糊图像进行去噪处理,其次利用导向滤波对模糊图像进行优化,在窗口内进行求和运算,快速实现图像优化,最终结合模糊图像特征提取方法,建立模糊BP神经网络模型,通过隶属函数对图像数据特征进行分析与提取。实验结果表明,所提方法的图像特征提取运行时间较快,对图像特征识别率较高以及对模糊图像特征提取效果较好。

Traditional image feature extraction methods have many disadvantages, such as incomplete image edge details, slow feature extraction speed and poor effect, being caused by the failure to optimize the blurred image through guided filtering. Therefore, a fuzzy image feature extraction method based on optimized guided filtering is designed in this paper. First of all, the blurred image was denoised. Secondly, guided filtering was utilized to optimize the blurred image. Then, in the window, the summation operation was implemented to quickly optimize the image. Concurrently, the fuzzy image feature extraction method was also introduced to establish the fuzzy BP neural network model. Finally, according to the membership function, the image data features were analyzed and extracted. The experimental results show that this method has a fast image feature extraction running time, high image feature recognition rate and good fuzzy image feature extraction effect.