The surface detection of optical fiber connector belongs to precision instrument detection, accordingly, making it possible for the large amounts of dust in the factory environment that exerts detrimental influence on the recovery of optical fiber connector. Nonetheless, the current detection technology possesses long running time, poor retention ability for image details, and is problematic to overcome interference in the actual working environment. To this end, we propose a self-identification noise reduction technology based on optimised extreme learning machine. Firstly, the interference data is processed by dimensionality reduction. Secondly, select the dimensionality reduction data as the training data, and use the extreme learning machine optimised by AdaBoost algorithm to locate the noise. Ultimately, the positions of noise points are repaired by filtering algorithms. The experimental results demonstrate that the selfrecognition noise reduction algorithm based on AdaBoost-Elm is equipped with high noise recognition ability and its ANRR reaches 97. 33%. Additionally, the average value of BBS and NRIQAVR based on AdaBoost-Elm noise reduction algorithm are 131. 14 and 2. 61 respectively, which are better than global filtering algorithm. In the end, we simulate the factory environment and use mean filtering based on AdaBoost-Elm to perform 3D restoration test on the sharply polluted fiber optic probe under different light intensity conditions. It is found that its BBS reaches around 130 and its NRIQAVR is lower than 2. 57, which has apparent merits compared with the median filtering based on Elm.