In the procedure of surface defects detection for large-aperture aspherical optical elements, it is of vital significance to adjust the optical axis of the element to be coaxial with the mechanical spin axis accurately. Therefore, a machine vision method for eccentric error correction is proposed in this paper. Focusing on the severe defocus blur of reference crosshair image caused by the imaging characteristic of the aspherical optical element, which may lead to the failure of correction, an Adaptive Enhancement Algorithm (AEA) is proposed to strengthen the crosshair image. AEA is consisted of existed Guided Filter Dark Channel Dehazing Algorithm (GFA) and proposed lightweight Multi-scale Densely Connected Network (MDC-Net). The enhancement effect of GFA is excellent but time-consuming, and the enhancement effect of MDC-Net is slightly inferior but strongly real-time. As AEA will be executed dozens of times during each correction procedure, its real-time performance is very important. Therefore, by setting the empirical threshold of definition evaluation function SMD2, GFA and MDC-Net are respectively applied to highly and slightly blurred crosshair images so as to ensure the enhancement effect while saving as much time as possible. AEA has certain robustness in time-consuming performance, which takes an average time of 0.2721s and 0.0963s to execute GFA and MDC-Net separately on ten 200pixels 200pixels Region of Interest (ROI) images with different degrees of blur. And the eccentricity error can be reduced to within 10um by our method.

翻译：在大口径非球面光学元件表面缺陷检测过程中，精确调整光学元件光轴与机械旋转轴共轴至关重要。为此，本文提出一种用于偏心误差校正的机器视觉方法。针对非球面光学元件成像特性导致参考十字丝图像严重离焦模糊、进而可能造成校正失效的问题，本文提出一种自适应增强算法（Adaptive Enhancement Algorithm, AEA）以强化十字丝图像。AEA由现有引导滤波暗通道去雾算法（Guided Filter Dark Channel Dehazing Algorithm, GFA）和所提出的轻量级多尺度密集连接网络（Lightweight Multi-scale Densely Connected Network, MDC-Net）组成。GFA的增强效果优异但耗时较长，而MDC-Net的增强效果稍逊但实时性极强。由于每次校正过程中AEA需执行数十次，其实时性能至关重要。因此，通过设置清晰度评价函数SMD2的经验阈值，将GFA与MDC-Net分别应用于高度模糊与轻微模糊的十字丝图像，在增强效果与时间节省之间取得平衡。AEA在耗时性能上具有一定鲁棒性：对十幅不同模糊程度的200像素×200像素感兴趣区域（Region of Interest, ROI）图像分别执行GFA与MDC-Net，平均耗时分别为0.2721秒与0.0963秒。采用该方法可将偏心误差校正至10微米以内。