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.