基于 GA-BP 的改进高斯均值区域去噪技术

光纤连接器因其在光传输系统中的重要作用而备受关注，但是其表面附着的杂质会使复原的表面形貌出现噪点。而且现有的检测方法无法准确定位噪点，需要对其进行多次整体降噪处理，以此得到的图像细节保留能力较差。为此提出了一种基于遗传算法优化BP神经网络(GA-BP)的改进高斯均值区域去噪技术。首先，对干涉数据进行降维处理；其次，将降维后的数据作为神经网络的训练数据，利用神经网络对噪点进行定位；最后，采用改进的高斯均值滤波对三维图像的噪点位置进行滤波处理。结果表明，通过神经网络判别法得到的噪声像素点为2.45%,相比于阈值判别法具有较高的精度。并且通过改进的高斯均值滤波方法得到的方法噪声差值为474.7,峰值信噪比(PSNR)值为32.56。相比于均值和中值滤波方法，图像细节保持能力较高，复原图像噪点凸起明显减少。因此，它更适用于基于白光干涉原理的自动化检测。

Improved gaussian mean region denoising technology based on GA-BP

Optical fiber connectors have attracted much attention due to the essential role in optical transmission systems, but the impurities attached on the fiber surface will generate noise on the recovered morphology. Moreover, the existing detection methods cannot accurately locate the noise. It needs to be processed for multiple overall noise reduction, and the image detail retention ability obtained by this method is inadequate. To this end, we proposed an improved Gaussian mean region denoising technology based on GA-BP neural network. Firstly, the interference data is processed by dimensionality reduction. Secondly, select the dimensionality reduction data as the training data, and use the neural network to locate the noise. Finally, the improved Gaussian mean filter is used to filter the noise position of the three-dimensional image. Furthermore, the results show that the noise pixel obtained by the neural network discrimination method is 2. 45%, which is higher than the threshold discrimination method. And the noise difference obtained by the improved Gaussian mean filtering method is 474. 7, and the PSNR value is 32. 56. Compared with the mean and median filtering methods, the image detail retention ability is higher, and the restored image noise bulge is significantly reduced. Therefore, it is more suitable for automatic detection based on the principle of white light interference.